Abstract
Background
Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas project (TCGA) subsequently replicated these subtypes and identified a signature predictive of survival in high grade serous cancers. We previously validated these signatures for use in formalin-fixed paraffin embedded (FFPE) tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.
Methods
We modeled signature-specific tumor characteristics and epidemiological risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.
Results
We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (p=0.0008) and race (p=0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (p=0.024) and first degree family history of breast or ovarian cancer (p=0.034) among the 106 high grade serous cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.
Conclusions
These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.
Impact
This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.
Keywords: molecular signatures, ovarian cancer, epidemiology, tumor characteristics, TCGA
Introduction
Ovarian cancer is a heterogeneous disease as demonstrated through associations with family history of cancer, genetic risk and the histopathology of this disease. Additionally, ovarian cancer is more common among whites and more lethal among blacks. The most common histology is epithelial ovarian cancer comprising 95% of invasive cancers. Among the invasive epithelial ovarian cancers there are several subtypes of which the serous subtype is the most common followed by endometrioid, clear cell and mucinous cancers. Some risk factor associations are specific to histologic subtypes of this disease such as endometriosis and endometrioid/clear cell cancers (1) and smoking and mucinous cancers (2).
Tothill et al. (3) recently reported evidence for six distinct molecular subtypes of epithelial ovarian cancer based on microarray gene expression profiling of 285 serous and endometrioid tumors of the ovary, peritoneum and fallopian tube. Four of these expression subtypes corresponded to most of the high grade serous (HGS) cancers while the other two expression subtypes were enriched for low malignant potential and low-grade endometrioid subtypes. More recently The Cancer Genome Atlas project (TCGA) identified four gene expression “intrinsic subtypes” of HGS ovarian cancer based on patterns of gene expression (mesenchymal, immunoreactive, differentiated, proliferative) that are highly congruent with the subtypes described by Tothill et al. (4). In addition, a signature predictive of survival was identified within the group of HGS cancers. To date, there have been no reports examining the associations between molecular subtypes and patient demographic characteristics or epidemiologic risks factors.
We sought to determine whether the six molecular subtypes of epithelial ovarian cancer identified by Tothill et al. (3) and the TCGA-derived survival signature (4) are related to exposure, risk factor, or demographic variables in the population-based North Carolina Ovarian Cancer Study (NCOCS).
Materials and Methods
Study subjects
Study subjects are a subset of invasive epithelial ovarian cancer cases selected from participants in the North Carolina Ovarian Cancer Study (NCOCS), a population-based case-control study conducted in a 48-county region of North Carolina. A detailed description of the study has been published previously (5, 6). Briefly, newly diagnosed cases were identified through the North Carolina Central Cancer Registry using rapid case ascertainment and physician approval was obtained before the patients were contacted. Eligible cases, aged 20 to 74, were diagnosed with epithelial ovarian cancer between 1999 and 2007. All cases underwent standardized histopathologic review by the study pathologist (RCB) to confirm diagnosis. The response rate among eligible cases was 65 percent. Although the response rate was low, the distribution of histologic subtypes and stage at diagnosis did not appear to be significantly different among the responders compared to the non-responders.
Subjects represent a subset of those with invasive epithelial ovarian cancer enrolled in the NCOCS and were selected because they were diagnosed with invasive epithelial ovarian cancer and because of availability of fresh frozen tumor tissue and matched formalin fixed paraffin embedded (FFPE) tissue samples. Due to this requirement, all subjects in the current study who were diagnosed at Duke University Medical Center. There were 193 invasive epithelial ovarian cancer cases of which 106 were advanced stage, HGS ovarian cancers. This subset of subjects were found to have comparable distributions of age at diagnosis and epidemiologic risk factors compared to the remaining invasive ovarian cancer cases in the NCOCS. However, differences in the distributions of some characteristics were found in this subset, including lower proportions of African Americans and poorly differentiated cancers, and higher proportions of invasive serous and advanced stage cancer.
The protocol was approved by the Duke University Medical Center Institutional Review Board and the human subjects committees at the Central Cancer Registry and each hospital where cases were identified.
Data and biospecimen collection
Nurse-interviewers obtained written informed consent, administered a standardized questionnaire to all NCOCS participants and drew a blood sample. A FFPE tissue sample was obtained for 95% of cases enrolled in the NCOCS. During the in-person interview anthropometric measurements including height, weight, and waist and hip circumferences were also obtained. The questionnaire included information on established and suspected ovarian cancer risk factors including family history of cancer, menstrual characteristics, reproductive history, infertility, hormone use and lifestyle characteristics such as smoking, alcohol consumption and physical activity. A more detailed description of the data collection process was published previously (6).
Body mass index (BMI) was calculated as weight one year prior to diagnosis in kg/(height in meters)2 and was categorized as <25, 25 - <30, 30-<35,and ≥35 kg/m2. Other variables included in the analysis are age at diagnosis (20-49 years, 50-59 years, 60-69 years, and 70-74 years), race (African American versus non-African American), menopausal status (pre- or post-menopausal) age at last pregnancy (never pregnant, < 24 years, 24-29 years, 30-34 years, ≥ 35 years), number of live births (none, 1-2, 3-4, ≥5), age at menarche (< 12, ≥12), a diagnosis of endometriosis (yes or no), years of oral contraceptive (never, <1, 1 - <5, ≥5), months breastfed (never, < 6, 6-12, > 12), family history of breast or ovarian cancer in a first degree relative (yes or no), and tubal ligation (yes or no). Characteristics of the tumor that were included in the analysis were diagnosis of primary site (ovary or peritoneum), tumor histologic subtype (serous, endometrioid, mucinous, clear cell, or other), tumor grade (well differentiated, moderately differentiated, and poorly differentiated) and stage at diagnosis (I/II or III/IV).
Gene expression analysis
All aspects of the generation of the gene expression data from these specimens are detailed in Sfakianos et al. Sfakianos 2013}. This publication describes a series of quality control steps for RNA extraction and derivation of gene expression using the whole genome Illumina DASL platform (HumanRef-8 Bead Chip). For a subset (n=43) of these samples, RNA from fresh frozen tissue and paired archival FFPE derived RNA were compared (Affymetrix U133A for the frozen tissue versus DASL for the FFPE). Briefly, the Affymetrix and Illumina data were preprocessed using the RMA (7) and limma (8) R/Bioconductor packages, respectively. DASL data for thirteen of the 43 paired samples and 4 of the NCOCS samples failed laboratory quality control and were removed from the analysis prior to preprocessing, leaving 30 paired samples with which to evaluate cross-platform concordance and 193 NCOCS samples for the association study. Affymetrix and Illumina probes were mapped to one another by RefSeq ID and the data were collapsed to their median in cases of many-to-one-matches. The resulting sets of Affymetrix- and Illumina-derived expression data were standardized separately so that each probe in each set had mean zero and standard deviation one.
Construction of the subtype signatures is described in Sfakianos et al. (9). Briefly, we defined the signature of a cluster as a weighted sum of the normalized expression values for the over-expressed probes minus a weighted sum of the normalized expression values for the under-expressed probes published by Tothill et al. (4). In order to downweight poor performing DASL probes, we weighted each expression value in the signature by the square of its correlation between Affymetrix and DASL log expression estimates calculated using a concurrently analyzed external set of paired fresh frozen/FFPE samples after truncating negative correlations at zero. Hence, the larger the value of a tumor’s cluster signature, the more likely it is to be of that subtype. We defined the TCGA survival signature as the weighted sum of normalized gene-level DASL expression values with weights equal to the product of the associated gene’s published log hazard estimate (Supplementary Table 1) (4) and its squared Affymetrix/DASL correlation in the external paired samples. We normalized all signatures to have mean zero and standard deviation one.
Statistical analyses
We used multivariate response multiple regression models with unstructured error covariance matrices to simultaneously assess the relationship between ovarian cancer risk factors, tumor characteristics and the six molecular subtypes of ovarian cancer. The six signatures, normalized to have mean zero and standard deviation one across samples, served as the response variables in these models. Hence, regression coefficients are interpreted as the expected difference in the signature, measured in standard deviations, associated with a unit change in the covariate. We examined the associations with tumor characteristics collectively, while controlling for age at diagnosis. We carried out these analyses for all ovarian cancer cases in the study and in the subset of advanced stage (stage III/IV), high grade (moderately to poorly differentiated) serous invasive cases. We also conducted analyses for each ovarian cancer risk factor individually while controlling for age for all histologic subtypes combined and restricting the analysis to high grade advanced stage serous cancer.
To this end, we used a likelihood ratio test to assess evidence in favor of association between each risk factor and tumor characteristic variable of interest and the ensemble of subtype signatures. In particular, we tested H0: the signatures are conditionally independent of the variable given age versus H1: the signatures are conditionally dependent on the variable given age. The coefficients of the variable in the H1 model were signature-specific as were all remaining parameters, including the intercepts, in the H0 and H1 models.
In addition, we used multivariable linear regression to model relationships between the TCGA survival signature and tumor characteristics and epidemiologic risk factors. We similarly conducted multivariable linear regression analyses to examine signature-specific associations between tumor characteristics and epidemiologic factors for each of the six subtype signatures defined by Tothill et al.(3)
Results
Tothill et al. (3) used unsupervised hierarchical clustering to assign epithelial ovarian cancers into 6 subtypes (C1-C6). These subtypes show enrichment for certain molecular and histologic features and were labeled accordingly: C1 are tumors with a high stromal content, C2 is defined primarily by an immune signature associated with tumor inflammatory response, the C3 signature is characterized as well differentiated and contains all of the low malignant potential cancers in the study but also some invasive serous cancers, C4 is an expression cluster that is characterized by the absence of other strong signatures and was labeled as cancers with a low stromal response, the C5 cluster was called mesenchymal or poorly differentiated and these are all HGS cancers, while C6 are low grade cancers with endometrioid features. These categories are congruent with the four TCGA expression subtypes that were defined on HGS cancers only. The TCGA subtypes include differentiated which overlaps with C3 (LMP) and C4 (low stroma), mesenchymal which is analogous to the Tothill C1 high stromal signature, immunoreactive which contains most of the inflammatory signature associated with C2, and proliferative characterized by genes associated with cell division some of which can be found in Tothill C5 but also present in other HGS categories (C1, C2, and C4) (4). Conversely, the two low grade Tothill categories, C3 and C6 are characterized by a low proliferation signature.
For the current study, we derived quantitative signatures for each tumor belonging to a specific Tothill cluster (C1-C6), hence class membership is not a categorical yes or no. To determine whether the class signatures represent non-overlapping biologic properties, we determined correlations between classes. If all cancers demonstrate unique class characteristics, then strong negative correlations would be expected. While the stronger correlations between the molecular clusters we observe tend to be negative (see Table 1a and 1b), we do estimate some positive correlations, notably between the C6 signature and the C3, C4 and C5 signatures, indicating that not all signatures are mutually exclusive. This is consistent with the recent analysis of the TCGA data and indicates that membership in an expression subtype is not rigid and cancers can have characteristics of multiple classes (10). All subsequent analyses employ this approach where each cancer is assigned a value for each cluster based on the combination of gene expression values that define that cluster.
TABLE 1a.
Pearson correlation coefficients and (p-values) for molecular clusters (C1-C6) and the TCGA survival signature in invasive ovarian cancers (N=193)*
| C1 | C2 | C3 | C4 | C5 | C6 | TCGA survival signature |
|
|---|---|---|---|---|---|---|---|
| C1 | 1.00 | −0.024 (0.741) |
−0.164 (0.023) |
−0.931 (<0.0001) |
−0.461 (<0.0001) |
−0.423 (<0.0001) |
−0.012 (0.873) |
| C2 | 1.00 | −0.482 (<0.0001) |
0.221 (0.002) |
−0.687 (<0.0001) |
−0.567 (<0.0001) |
−0.217 (0.002) |
|
| C3 | 1.00 | −0.039 (0.590) |
0.086 (0.233) |
0.488 (<0.0001) |
−.0202 (0.005) |
||
| C4 | 1.00 | 0.320 (<0.0001) |
0.205 (<0.004) |
0.013 (0.862) |
|||
| C5 | 1.00 | 0.616 (<0.0001) |
0.424 (<0.0001) |
||||
| C6 | 1.00 | 0.158 (0.028) |
C1: High stromal response, C2: Immunoreactive, C3: Well differentiated, C4: Low stromal response, C5: Mesenchymal/undifferentiated, C6: Endometrioid.
TABLE 1b.
Pearson correlation coefficients and (p-values) for molecular clusters (C1-C6) and the TCGA survival signature in advanced-stage, high grade serous ovarian cancers (N=106)*
| C1 | C2 | C3 | C4 | C5 | C6 | TCGA survival signature |
|
|---|---|---|---|---|---|---|---|
| C1 | 1.00 | −0.067 (0.497) |
−0.049 (0.617) |
−0.949 (<0.0001) |
−0.505 (<0.0001) |
−0.362 (0.0001) |
−0.182 (0.062) |
| C2 | 1.00 | −0.380 (<0.0001) |
0.208 (0.033) |
−0.710 (<0.0001) |
−0.702 (<0.0001) |
−0.153 (0.118) |
|
| C3 | 1.00 | −0.055 (0.579) |
0.110 (0.260) |
0.463 (<0.0001) |
−.0228 (0.019) |
||
| C4 | 1.00 | 0.364 (0.0001) |
0.201 (0.039) |
0.158 (0.105) |
|||
| C5 | 1.00 | 0.825 (<0.0001) |
0.387 (<0.0001) |
||||
| C6 | 1.00 | 0.220 (0.024) |
C1: High stromal response, C2: Immunoreactive, C3: Well differentiated, C4: Low stromal response, C5: Mesenchymal/undifferentiated, C6: Endometrioid.
The distributions of patient and tumor characteristics for invasive cases (n = 193) and for the subset of advanced stage HGS tumors (n = 106) are found in Table 2. Most of the tumors were serous ovarian cancer (65.3%), stage III/IV at diagnosis (80.8%) and moderately to poorly differentiated at diagnosis (84.9%). Only 29 women (15%) in our sample reported their race as African American, with the majority of subjects reporting their race as Caucasian (81.8%). Eighty percent of the ovarian cancer cases were 50 years of age or older at diagnosis. Of the 193 cases in the study, 18.1% were nulliparous, 40.4% never used oral contraceptives (OCs) and 23.8% of the study subjects reported a family history of breast or ovarian cancer in a first degree relative.
TABLE 2.
Frequency distribution of patient and tumor characteristics among invasive epithelial ovarian cancers and a subset of advanced-stage, high-grade serous ovarian cancer cases
| Variable | Invasive ovarian cancer cases N=193 |
Advanced- stage, high- grade serous ovarian cancer cases N=106 |
|---|---|---|
| Age (yrs) | ||
| 0-49 | 38 (19.7) | 15 (14.2) |
| 50-59 | 67 (34.7) | 40 (37.7) |
| 60-69 | 62 (32.1) | 38 (35.8) |
| ≥70 | 26 (13.5) | 13 (12.3) |
| Self-reported race | ||
| Caucasian | 156 (81.8) | 91 (85.8) |
| African American | 29 (15.0) | 13 (12.3) |
| Other | 8 (4.2) | 2 (1.9) |
| Smoking Status | ||
| Never smoked | 102 (52.85) | 58 (54.72) |
| Current smoker | 29 (15.03) | 16 (15.09) |
| Ex-smoker | 62 (32.12) | 32 (30.19) |
| Diagnosed infertility | ||
| Yes | 22 (11.4) | 12 (11.3) |
| No | 171 (88.6) | 94 (88.7) |
| Age at last pregnancy | ||
| Never | 28 (14.5) | 13 (12.3) |
| <24 | 39 (20.2) | 22 (20.7) |
| 25-29 | 56 (29.0) | 31 (29.3) |
| 30-34 | 41 (21.2) | 19 (17.9) |
| ≥35 | 29 (15.0) | 21 (19.8) |
| Number of live births (yrs) | ||
| None | 35 (18.1) | 18 (17.0) |
| 1-2 | 95 (49.2) | 53 (50.0) |
| 3-4 | 55 (28.5) | 31 (29.2) |
| ≥5 | 8 (4.2) | 4 (3.8) |
| Age at menarche | ||
| <12 | 45 (23.4) | 20 (18.9) |
| >12 | 147 (76.6) | 86 (81.1) |
| Missing | 1 | |
| Menopausal status at diagnosis | ||
| Premenopausal | 50 (26.0) | 25 (23.6) |
| Postmenopausal | 142 (74.0) | 81 (76.4) |
| Missing | 1 | |
| Ever diagnosed with endometriosis | ||
| Yes | 18 (9.4) | 7 (6.6) |
| No | 173 (90.6) | 99 (93.4) |
| missing | 2 | |
| Oral contraceptive use (yrs) | ||
| Never | 76 (40.4) | 38 (37.3) |
| <1 | 24 (12.8) | 13 (12.7) |
| 1-<5 | 42 (22.3) | 25 (24.5) |
| ≥5 | 46 (24.5) | 26 (25.5) |
| Missing | 5 | 4 |
| Breastfeeding (among ever pregnant) | ||
| Never | 90 (57.0) | 52 (59.1) |
| <6 months | 33 (20.9) | 17 (19.3) |
| 6-12 months | 14 (8.8) | 7 (8.0) |
| >12 months | 21 (13.3) | 12 (13.6) |
| Tubal ligation | ||
| Yes | 52 (26.9) | 32 (30.2) |
| No | 141 (73.1) | 74 (69.8) |
| BMI (Kg/m2) 1year prior to diagnosis | ||
| <25 | 75 (39.9) | 45 (44.1) |
| 25-<30 | 49 (26.1) | 26 (25.5) |
| 30-<35 | 29 (15.4) | 17 (16.7) |
| >35 | 35 (18.6) | 14 (13.7) |
| Missing | 5 | 4 |
| Family history of breast and ovarian cancers | ||
| Yes | 46 (23.8) | 24 (22.6) |
| No | 147 (76.2) | 82 (77.4) |
| Peritoneal | ||
| Yes | 19 (9.8) | 16 (15.1) |
| No | 174 (90.2) | 90 (84.9) |
| Histologic subtype | ||
| Serous | 126 (65.3) | 106 (100) |
| Endometrioid | 25 (13.0) | 0 (0) |
| Mucinous | 7 (3.6) | 0 (0) |
| Clear cell | 9 (4.7) | 0 (0) |
| Other | 26 (13.5) | 0 (0) |
| Tumor grade | ||
| Well differentiated | 29 (15.1) | 0 (0) |
| Moderate | 67 (34.9) | 54 (50.9) |
| Poor/undifferentiated | 96 (50.0) | 52 (49.1) |
| Missing | 1 | 0 |
| Tumor stage | ||
| I/II | 37 (19.2) | 0 (0) |
| III/IV | 156 (80.8) | 106 (100) |
In the subset of women diagnosed with advanced stage, HGS tumors, compared to the combined histologic subtypes, a lower proportion had an early age at menarche less (<12 years), 19% versus 23.4% and a higher proportion have a primary site of the peritoneum, 10% versus 15%, respectively.
Tumor characteristics
The mixed model testing for heterogeneity includes age at diagnosis, histologic subtype, cancer site, stage and tumor grade for all histologic subtypes and age at diagnosis, cancer site, and tumor grade for the model restricted to advanced stage HGS cancers. As shown in Table 3, among all histologic subtypes of epithelial ovarian cancer, having a diagnosis of primary peritoneal cancer was more associated with molecular signatures related to high stromal response (C1) and immune reaction (C2) clusters and less likely among the low stromal (C4) and mesenchymal/undifferentiated (C5) clusters (p= 0.001). These clusters were also more likely to have a diagnosis of the serous histologic subtype compared to the other four molecular signatures (p <0.0001). Those with signatures consistent with the well differentiated (C3), the low stromal response (C4), the mesenchymal/undifferentiated (C5), and the endometrioid (C6) clusters were more likely to have been diagnosed with a non-serous histologic subtype and less like to have a diagnosis of primary peritoneal cancer. Poor tumor grade is consistent with clusters C1, C2 and C4, while well differentiated tumors were observed more frequently in the C3 and C6 signatures (p < 0.0001). Similar patterns were observed restricting the analysis to the advanced stage, HGS cases. Advanced stage at diagnosis is positively correlated with the high stromal response (C1) cluster, and inversely associated with signatures consistent with the well differentiated (C3) and the endometrioid (C6) clusters (p= 0.006). Heterogeneity in the age at diagnosis between the six signatures was also found for the combined histologic subtypes (p=0.0008) and when the analysis was restricted to advanced stage, HGS (p= 0.0161). Older age at diagnosis was consistent with cancers that display the high stromal response (C1) and the mesenchymal/undifferentiated (C5) signatures while an earlier age at diagnosis was consistent with the immune (C2), the well differentiated (C3), and the low stromal response (C4) clusters.
TABLE 3.
Multivariate mixed model tests for heterogeneity of relationships between molecular signatures* and age at diagnosis and tumor characteristics**
| C1 | C2 | C3 | C4 | C5 | C6 | Mixed model p- value |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||||||
| Variable | β | SE | β | SE | β | SE | β | SE | β | SE | β | SE | |
| All invasive ovarian cancer cases | |||||||||||||
| Age at diagnosis (years) | |||||||||||||
| 20-49 | referent | referent | referent | referentb | referent | referent | 0.0008 | ||||||
| 50-59 | 0.153 | 0.184 | −0.267 | 0.193 | −0.065 | 0.154 | −0.333 | 0.190 | 0.411 | 0.197 | 0.153 | 0.155 | |
| 60-69 | 0.052 | 0.188 | −0.078 | 0.197 | −0.256 | 0.157 | −0.229 | 0.194 | 0.314 | 0.201 | 0.162 | 0.158 | |
| ≥70 | 0.575 | 0.238 | −0.275 | 0.249 | −0.032 | 0.198 | −0.858 | 0.245 | 0.183 | 0.254 | −0.068 | 0.200 | |
| Histologic subtype | |||||||||||||
| Serous | referent | referent | referent | referent | referent | referentc | <.0001 | ||||||
| Clear cell | 0.549 | 0.348 | −0.630 | 0.365 | 0.723 | 0.290 | −0.647 | 0.358 | 0.075 | 0.372 | 0.657 | 0.292 | |
| Endometrioid | −0.346 | 0.210 | −0.150 | 0.220 | 0.023 | 0.175 | 0.228 | 0.216 | 0.295 | 0.224 | 1.261 | 0.176 | |
| Mucinous | −0.298 | 0.372 | −0.160 | 0.390 | −0.133 | 0.310 | 0.361 | 0.383 | 0.423 | 0.398 | 1.131 | 0.312 | |
| Other | −0.247 | 0.212 | −0.367 | 0.222 | 0.058 | 0.177 | 0.185 | 0.218 | 0.569 | 0.227 | 0.783 | 0.178 | |
| Primary cancer site | |||||||||||||
| Ovary | referentb | referent | referent | referentb | referentb | referent | 0.0011 | ||||||
| Peritoneal | 0.860 | 0.228 | 0.311 | 0.239 | −0.243 | 0.190 | −0.687 | 0.234 | −0.784 | 0.243 | −0.285 | 0.191 | |
| Stage at diagnosis | |||||||||||||
| TNMstage I/II | referenta | referent | referent | referent | referent | referenta | 0.0061 | ||||||
| TNMstage III/IV | 0.465 | 0.203 | −0.081 | 0.213 | −0.152 | 0.169 | −0.270 | 0.209 | −0.078 | 0.217 | −0.375 | 0.170 | |
| Tumor grade | |||||||||||||
| Poor/undifferentiated | referent | referentc | referentc | referent | referent | referentb | <.0001 | ||||||
| Moderately differentiated | 0.023 | 0.157 | −0.672 | 0.164 | 0.477 | 0.131 | −0.157 | 0.161 | 0.365 | 0.168 | 0.225 | 0.132 | |
| Well differentiated | 0.029 | 0.217 | −1.044 | 0.228 | 1.886 | 0.181 | −0.454 | 0.224 | 0.091 | 0.232 | 0.703 | 0.182 | |
|
Advanced-stage, high-grade serous ovarian
cancer cases |
|||||||||||||
| Age at diagnosis (years) | |||||||||||||
| 20-49 | referent | referent | referent | referent | referent | referent | 0.0161 | ||||||
| 50-59 | 0.045 | 0.278 | 0.016 | 0.264 | −0.049 | 0.186 | −0.214 | 0.288 | 0.286 | 0.284 | 0.046 | 0.166 | |
| 60-69 | 0.056 | 0.279 | 0.188 | 0.265 | −0.271 | 0.187 | −0.239 | 0.289 | 0.157 | 0.285 | −0.018 | 0.167 | |
| ≥70 | 0.436 | 0.355 | −0.447 | 0.337 | 0.286 | 0.237 | −0.828 | 0.367 | 0.356 | 0.362 | 0.316 | 0.212 | |
| Primary cancer site | |||||||||||||
| Ovary | referentb | referenta | referent | referenta | referentb | referentb | 0.0013 | ||||||
| Peritoneal | 0.830 | 0.257 | 0.568 | 0.244 | −0.325 | 0.172 | −0.589 | 0.266 | −0.978 | 0.262 | −0.487 | 0.154 | |
| Tumor grade | |||||||||||||
| Poor/undifferentiated | referent | referentb | referentb | referent | referenta | referentb | 0.0003 | ||||||
| Moderately differentiated | −0.091 | 0.187 | −0.703 | 0.177 | 0.482 | 0.125 | −0.011 | 0.193 | 0.477 | 0.190 | 0.308 | 0.112 | |
Multivariable regression analysis significance levels: p<0.05
p<0.01
p<0.0001.
C1: High stromal response, C2: Immunoreactive, C3: Well differentiated, C4: Low stromal response, C5: Mesenchymal/undifferentiated, C6: Endometrioid.
Regression coefficients are the expected difference in the signature, measured in standard deviations, associated with unit change in the covariate.
Epidemiologic risk factors
Among all histologic subtypes combined (Table 4), after adjusting for age at diagnosis, the high stromal cluster (C1) was found to be inversely associated with a race of African American compared to non-African American, while those with molecular signatures consistent with the mesenchymal/undifferentiated (C5) and endometrioid (C6) clusters were more likely to be African American (p = 0.008). Women with well differentiated (C3) and endometrioid (C6) type tumors were more likely to have reported a diagnosis of endometriosis while the mesenchymal/ undifferentiated (C4) tumors were inversely associated with endometriosis (p=0.056).
TABLE 4.
Multivariate mixed model tests for heterogeneity of relationships between molecular signatures* and epidemiologic risk factors adjusted for age at diagnosis for 193 invasive epithelial ovarian cancers of combined histologic subtypes**
| C1 | C2 | C3 | C4 | C5 | C6 | Mixed model p- value |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||||||
| Variable | β | SE | β | SE | β | SE | β | SE | β | SE | β | SE | |
| Race | |||||||||||||
| Non-African American | referenta | referent | referent | referent | referentb | referenta | 0.008 | ||||||
| African American | −0.440 | 0.196 | −0.180 | 0.202 | 0.183 | 0.198 | 0.261 | 0.196 | 0.553 | 0.198 | 0.489 | 0.198 | |
| Age at menarche | |||||||||||||
| >=12 | referent | referent | referent | referent | referent | referent | 0.611 | ||||||
| 0-<12 | 0.097 | 0.172 | −0.200 | 0.173 | −0.026 | 0.169 | −0.161 | 0.170 | 0.217 | 0.174 | 0.094 | 0.174 | |
| Menopausal status | |||||||||||||
| Pre-menopausal | referent | referent | referent | referent | referent | referent | 0.176 | ||||||
| Post-menopausal | −0.079 | 0.229 | −0.189 | 0.232 | −0.283 | 0.225 | 0.012 | 0.228 | 0.233 | 0.233 | 0.205 | 0.232 | |
| Number of live births | |||||||||||||
| None | referent | referent | referent | referent | referent | referent | 0.151 | ||||||
| 1-2 | 0.104 | 0.196 | −0.159 | 0.200 | −0.153 | 0.197 | −0.033 | 0.194 | −0.046 | 0.200 | −0.045 | 0.199 | |
| 3-4 | 0.368 | 0.219 | −0.136 | 0.224 | −0.118 | 0.220 | −0.298 | 0.217 | −0.077 | 0.224 | −0.155 | 0.223 | |
| ≥5 | 0.276 | 0.391 | −0.370 | 0.401 | −0.170 | 0.394 | −0.118 | 0.388 | 0.027 | 0.401 | 0.260 | 0.399 | |
| Age at last pregnancy (years) | |||||||||||||
| 25-29 | referent | referent | referent | referent | referenta | referenta | 0.253 | ||||||
| 14-24 | −0.318 | 0.205 | −0.322 | 0.209 | −0.059 | 0.193 | 0.315 | 0.206 | 0.454 | 0.214 | 0.239 | 0.201 | |
| 30-34 | −0.297 | 0.200 | −0.196 | 0.204 | 0.009 | 0.189 | 0.367 | 0.201 | 0.267 | 0.208 | 0.144 | 0.196 | |
| ≥35 | 0.162 | 0.224 | 0.178 | 0.228 | −0.264 | 0.211 | 0.009 | 0.225 | −0.218 | 0.233 | −0.425 | 0.220 | |
| Breasfeeding (months) | |||||||||||||
| Never | referent | referenta | referent | referent | referenta | referent | 0.380 | ||||||
| <6 | −0.171 | 0.207 | 0.185 | 0.202 | −0.051 | 0.194 | 0.195 | 0.208 | −0.089 | 0.203 | −0.220 | 0.201 | |
| 6-12 | −0.146 | 0.295 | 0.245 | 0.287 | −0.009 | 0.277 | 0.197 | 0.297 | −0.123 | 0.289 | −0.202 | 0.286 | |
| ≥12 | −0.441 | 0.241 | −0.627 | 0.235 | 0.127 | 0.226 | 0.289 | 0.243 | 0.704 | 0.236 | 0.461 | 0.234 | |
| Duration of oral contraceptive use (years) | |||||||||||||
| Never | referent | referent | referent | referent | referent | referent | 0.374 | ||||||
| 0-<1 | 0.097 | 0.229 | 0.554 | 0.231 | −0.222 | 0.229 | −0.006 | 0.227 | −0.479 | 0.235 | −0.377 | 0.233 | |
| 1-<5 | 0.082 | 0.188 | 0.112 | 0.190 | −0.135 | 0.188 | −0.029 | 0.187 | −0.077 | 0.193 | −0.360 | 0.191 | |
| ≥5 | 0.411 | 0.183 | 0.000 | 0.185 | −0.131 | 0.183 | −0.336 | 0.182 | −0.102 | 0.188 | −0.163 | 0.186 | |
| Doctor diagnosis of infertility | |||||||||||||
| No | referent | referent | referent | referenta | referent | referent | 0.460 | ||||||
| Yes | 0.426 | 0.222 | −0.209 | 0.228 | 0.059 | 0.224 | −0.477 | 0.220 | −0.122 | 0.227 | −0.143 | 0.227 | |
| Body mass index (Kg/m2) | |||||||||||||
| 25-< 30 | referent | referent | referent | referent | referent | referent | 0.218 | ||||||
| 13-<25 | 0.284 | 0.180 | 0.206 | 0.182 | −0.102 | 0.180 | −0.155 | 0.179 | −0.386 | 0.181 | −0.309 | 0.179 | |
| 30-<35 | 0.160 | 0.230 | −0.037 | 0.233 | −0.016 | 0.230 | −0.030 | 0.229 | −0.088 | 0.232 | −0.290 | 0.229 | |
| ≥35 | 0.245 | 0.219 | 0.095 | 0.221 | −0.037 | 0.219 | −0.166 | 0.218 | −0.113 | 0.220 | 0.141 | 0.218 | |
| Tubal ligation | |||||||||||||
| No | referent | referenta | referent | referent | referent | referent | 0.270 | ||||||
| Yes | 0.062 | 0.160 | 0.342 | 0.161 | −0.080 | 0.160 | 0.048 | 0.159 | −0.277 | 0.162 | −0.311 | 0.161 | |
| Endometriosis | |||||||||||||
| No | referent | referent | referent | referent | referent | referent | 0.056 | ||||||
| Yes | −0.055 | 0.244 | −0.116 | 0.249 | 0.195 | 0.242 | −0.216 | 0.241 | 0.027 | 0.246 | 0.263 | 0.246 | |
| Smoking status | |||||||||||||
| Never smoked | referent | referent | referent | referent | referent | referent | 0.833 | ||||||
| Current smoker | −0.051 | 0.206 | 0.015 | 0.210 | 0.278 | 0.205 | 0.049 | 0.204 | −0.034 | 0.210 | 0.169 | 0.209 | |
| Ex-smoker | 0.116 | 0.160 | −0.004 | 0.163 | −0.027 | 0.159 | −0.116 | 0.159 | 0.033 | 0.163 | 0.088 | 0.162 | |
| Family history of breast or ovarian cancers | |||||||||||||
| No | referent | referent | referent | referent | referent | referent | 0.961 | ||||||
| Yes | 0.037 | 0.165 | 0.018 | 0.168 | 0.046 | 0.165 | −0.049 | 0.164 | −0.002 | 0.168 | −0.050 | 0.167 | |
Multivariable regression analysis significance levels: p<0.05
p<0.01
p<0.0001.
C1: High stromal response, C2: Immunoreactive, C3: Well differentiated, C4: Low stromal response, C5: Mesenchymal/undifferentiated, C6: Endometrioid.
Regression coefficients are the expected difference in the signature, measured in standard deviations, associated with unit change in the covariate.
We performed the same analyses restricting the sample to advanced stage, HGS ovarian cancers (n=106, Table 5). Similar to the finding for all histologic subtypes, African Americans were less likely to have a molecular signature consistent with the high stromal response (C1) and immune (C2) clusters but more likely to have a molecular signature consistent with the remaining four clusters (p = 0.003). Among the advanced stage, HGS cases, months of breastfeeding was found to be differentially associated with the molecular signatures, with an inverse relationship with the high grade stromal (C1) and the immune (C2) clusters and a positive relationship with the low stromal response (C4), the mesenchymal/undifferentiated (C5) and the endometrioid (C6) clusters (p=0.024). Family history of breast or ovarian cancer in a first degree relative wass more likely to be observed among those with signatures consistent with the high stromal response (C1) and the immunoreactive (C2) clusters, while less likely reported by those with molecular signatures consistent with the remaining four clusters (C3-C6) (p = 0.034). Additionally, a borderline statistical difference (p=0.071) between molecular signatures was observed with OC use. However, these relationships were not monotone with increasing duration of OC use. No heterogeneity in the associations with the molecular signatures and endometriosis was detected among HGS cases. This is consistent with eliminating the low grade and non-serous cancers from this analysis.
TABLE 5.
Multivariate mixed model tests for heterogeneity of relationships between molecular signatures* and epidemiologic risk factors adjusted for age at diagnosis for 106 advanced-stage, high-grade serous ovarian cancers**
| C1 | C2 | C3 | C4 | C5 | C6 | Mixed model p- value |
|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|||||||||||||
| Variable | β | SE | β | SE | β | SE | β | SE | β | SE | β | SE | |
| Race | |||||||||||||
| Non-African American | referentb | referent | referent | referentb | referentb | referentb | 0.003 | ||||||
| African American | −0.979 | 0.272 | −0.461 | 0.278 | 0.115 | 0.197 | 0.864 | 0.279 | 1.112 | 0.281 | 0.606 | 0.165 | |
| Age at menarche | |||||||||||||
| >=12 | referent | referent | referent | referent | referent | referent | 0.559 | ||||||
| 0-<12 | −0.272 | 0.244 | −0.261 | 0.239 | −0.082 | 0.167 | 0.256 | 0.246 | 0.448 | 0.253 | 0.117 | 0.149 | |
| Menopausal status | |||||||||||||
| Pre-menopausal | referent | referent | referent | referent | referent | referent | 0.733 | ||||||
| Post-menopausal | −0.015 | 0.276 | −0.030 | 0.270 | −0.298 | 0.187 | 0.037 | 0.279 | 0.154 | 0.289 | 0.013 | 0.168 | |
| Number of live births | |||||||||||||
| None | referent | referent | referent | referent | referent | referent | 0.344 | ||||||
| 1-2 | 0.065 | 0.260 | −0.125 | 0.261 | −0.242 | 0.180 | 0.051 | 0.264 | 0.119 | 0.274 | −0.024 | 0.161 | |
| 3-4 | 0.150 | 0.289 | −0.101 | 0.289 | −0.043 | 0.200 | −0.110 | 0.293 | 0.138 | 0.304 | 0.058 | 0.178 | |
| ≥5 | 1.197 | 0.538 | 0.102 | 0.539 | −0.295 | 0.372 | −0.987 | 0.547 | −0.949 | 0.566 | −0.396 | 0.332 | |
| Age at last pregnancy (years) | |||||||||||||
| 25-29 | referent | referent | referent | referent | referent | referent | 0.534 | ||||||
| 14-24 | −0.491 | 0.274 | −0.347 | 0.270 | −0.172 | 0.165 | 0.456 | 0.274 | 0.666 | 0.289 | 0.248 | 0.165 | |
| 30-34 | −0.187 | 0.299 | −0.097 | 0.295 | −0.194 | 0.180 | 0.242 | 0.300 | 0.336 | 0.315 | 0.043 | 0.181 | |
| ≥35 | 0.143 | 0.276 | −0.146 | 0.272 | 0.026 | 0.166 | −0.133 | 0.276 | −0.061 | 0.291 | −0.009 | 0.167 | |
| Breasfeeding (months) | |||||||||||||
| Never | referent | referentb | referent | referent | referenta | referenta | 0.024 | ||||||
| <6 | −0.324 | 0.281 | 0.385 | 0.253 | −0.095 | 0.173 | 0.323 | 0.287 | −0.075 | 0.281 | −0.133 | 0.160 | |
| 6-12 | −0.226 | 0.411 | 0.117 | 0.370 | −0.083 | 0.253 | 0.206 | 0.420 | −0.016 | 0.411 | −0.061 | 0.234 | |
| ≥12 | −0.644 | 0.314 | −0.957 | 0.283 | 0.098 | 0.193 | 0.351 | 0.321 | 1.063 | 0.314 | 0.549 | 0.178 | |
| Duration of oral contraceptive use (years) | |||||||||||||
| Never | referenta | referent | referent | referent | referent | referent | 0.071 | ||||||
| 0-<1 | −0.178 | 0.298 | 0.652 | 0.297 | −0.233 | 0.212 | 0.210 | 0.301 | −0.291 | 0.326 | −0.112 | 0.188 | |
| 1-<5 | −0.266 | 0.247 | 0.120 | 0.246 | 0.048 | 0.176 | 0.231 | 0.250 | 0.167 | 0.271 | 0.221 | 0.156 | |
| ≥5+ | 0.452 | 0.236 | 0.024 | 0.235 | −0.228 | 0.168 | −0.418 | 0.239 | −0.059 | 0.259 | 0.071 | 0.149 | |
| Doctor diagnosis of infertility | |||||||||||||
| No | referent | referent | referent | referent | referent | referent | 0.736 | ||||||
| Yes | 0.428 | 0.296 | 0.002 | 0.292 | 0.029 | 0.204 | −0.420 | 0.299 | −0.341 | 0.311 | −0.211 | 0.180 | |
| Body mass index (Kg/m2) | |||||||||||||
| 25-< 30 | referent | referent | referent | referent | referent | referent | 0.221 | ||||||
| 13-<25 | 0.101 | 0.236 | 0.222 | 0.233 | 0.029 | 0.163 | 0.047 | 0.239 | −0.379 | 0.251 | −0.222 | 0.145 | |
| 30-<35 | −0.315 | 0.295 | 0.309 | 0.292 | 0.096 | 0.204 | 0.541 | 0.299 | −0.084 | 0.313 | −0.028 | 0.181 | |
| ≥35 | 0.373 | 0.323 | 0.004 | 0.319 | 0.382 | 0.223 | −0.194 | 0.327 | −0.436 | 0.343 | −0.113 | 0.198 | |
| Tubal ligation | |||||||||||||
| No | referent | referent | referent | referent | referent | referent | 0.435 | ||||||
| Yes | 0.250 | 0.212 | 0.340 | 0.206 | −0.024 | 0.146 | −0.191 | 0.215 | −0.388 | 0.220 | −0.132 | 0.129 | |
| Endometriosis | |||||||||||||
| No | referent | referent | referenta | referent | referent | referent | 0.311 | ||||||
| Yes | 0.230 | 0.378 | 0.009 | 0.371 | −0.535 | 0.254 | −0.307 | 0.382 | 0.068 | 0.396 | −0.109 | 0.230 | |
| Smoking status | |||||||||||||
| Never smoked | referent | referent | referent | referent | referent | referent | 0.194 | ||||||
| Current smoker | −0.178 | 0.272 | 0.361 | 0.264 | 0.019 | 0.186 | 0.233 | 0.274 | −0.003 | 0.284 | 0.005 | 0.166 | |
| Ex-smoker | 0.087 | 0.214 | −0.090 | 0.208 | 0.040 | 0.147 | −0.097 | 0.216 | 0.217 | 0.224 | 0.051 | 0.130 | |
| Family history of breast or ovarian cancers | |||||||||||||
| No | referent | referent | referent | referent | referent | referenta | 0.034 | ||||||
| Yes | 0.361 | 0.221 | 0.291 | 0.217 | −0.246 | 0.151 | −0.240 | 0.225 | −0.250 | 0.233 | −0.272 | 0.133 | |
Multivariable regression analysis significance levels: p<0.05
p<0.01
p<0.0001.
C1: High stromal response, C2: Immunoreactive, C3: Well differentiated, C4: Low stromal response, C5: Mesenchymal/undifferentiated, C6: Endometrioid.
Regression coefficients are the expected difference in the signature, measured in standard deviations, associated with unit change in the covariate.
As race, family history and age at diagnosis had significant and unexpected association with expression subtype in our data, we examined associations with race, BRCA1/2 mutation status and age at diagnosis in the TCGA data (4). Of the 489 TCGA subjects with expression data, 434 were white, 22 were black and 33 were of another race. We found no evidence for association between race and the subtype signatures as we have defined them here or between race and the TCGA subtype designations. However, we did find evidence of association in the TCGA data between both the subtype signatures and TCGA subtype calls and presence of a germline BRCA1 or BRCA2 mutation. BRCA1/2 mutation carriers had higher cluster of high stromal content (C1) and immune (C2) signatures (p=0.014 and p=0.680, respectively) and lower cluster C3-C6 signatures (p=0.160, 0.075, 0.088 and 0.008, respectively). These findings are consistent with our study demonstrating that first degree family history associated with clusters C1 and C2. In addition, the relationships we observed with age at diagnosis in the TCGA were similar to those in the NCOCS data. In particular, in the TCGA data, older age at diagnosis was associated with the C5 cluster (p=0.002) while younger age at diagnosis was associated with the C2 (p=0.060), C3 (p=0.012) and C4 (p=0.145) clusters.
The TCGA-derived survival signature
In Sfakianos et al (9), we showed that the TCGA survival signature was significantly (hazard ratio = 2.7; p=0.0007) associated with overall survival among the advanced stage, high grade NCOCS samples. Here we considered whether the TCGA survival signature represents a distinct etiologic phenotype of ovarian cancer. Analyses of associations with known prognostic characteristics may help to characterize whether the TCGA survival signature is an independent predictor of survival or a culmination of known prognostic factors (see supplemental Table 1). We performed multiple linear regression analyses examining relationships between the TCGA-derived survival signature and epidemiological risk factors and tumor characteristics. A diagnosis of an epithelial subtype other than a serous ovarian cancer and a poor/undifferentiated tumor grade were both positively associated with the TCGA signature. We did not observe a relationship with stage at diagnosis or tumor site and the TCGA signature although there was a positive trend with advanced stage at diagnosis. Most risk factors including age at diagnosis were not found to be significantly associated with the TCGA-derived survival signature. Among all histologic subtypes weak associations were seen with early age at menarche (<12 years) and decreased duration of oral contraceptive and the survival signature. These relationships were attenuated when the analysis was restricted to the HGS cases.
Discussion and Conclusion
Our results are consistent with the tumor characteristics of these clusters as defined by Tothill et al. (3). We show that early stage at diagnosis and well differentiated tumor grade is associated with the clusters classified with well differentiated (C3) and endometrioid (C6) features. We also found the serous histologic subtype to be positively associated with the high stromal content (C1) signature and by an immune signature (C2) compared to non-serous histologic subtypes while serous histology was inversely associated with molecular clusters with well differentiated and endometrioid features compared to other histologic subtypes. A novel finding is that a higher fraction of primary peritoneal cancers are consistent with the high stromal content (C1) signature compared to primary ovarian cancers, both for all histologic subtypes combined and among advanced stage HGS cases. Therefore, the differences noted between the subtypes and a diagnosis of primary peritoneal cancer is distinguished from the serous histologic subtype.
To our knowledge, this is the first study to examine correlations between ovarian cancer risk factors and molecular subtype signatures recently described by Tothill et al., (3) as well as the TCGA ovarian cancer survival signature (4). Of note, both age at diagnosis and race appear to differ according to the intrinsic clusters defined by Tothill et al. The age diagnosis of the patient is a well-documented prognostic factor for ovarian cancer outcomes, with older age associated with worse overall survival and presumably indicative of more aggressive disease (11). We observed positive associations between older age at diagnosis and clusters C1 and C5, the clusters identified by Tothill et al (3) to associate with the worst unadjusted survival prospects.
Although most of the established epidemiologic risk factors, including years of oral contraceptive use and pregnancy history, do not appear to be strongly differentially associated with the molecular clusters, a few associations were observed, particularly among the high grade advanced stage serous cases. Overall, heterogeneity is found with the association between cluster and a diagnosis of endometriosis among the combined histologic subtype but not when the analysis was restricted to HGS cases. We did not have the power to determine whether this association was driven by the clear cell and endometrioid cancer subtypes. Heterogeneity in the associations between months of breastfeeding and family history of breast and ovarian cancers and the molecular clusters became evident when the analysis was restricted to advanced stage, HGS tumors. Although there have been reports of risk factors associations, such as smoking and endometriosis, that are specific to histologic subtypes of ovarian cancer (1, 2), these results suggest molecular signatures of epithelial ovarian cancer may define etiologic pathways beyond that of histology alone.
There has been a growing realization that molecular subtypes of ovarian cancer do not define distinct disease entities as subtypes do with breast cancer but rather characterize distinct patterns of functionally significant molecular activities that my co-exist in varying ratios across tumors of the same histologic classification (9, 10). While classifying a tumor by its dominant signature ignores these complexities, one would expect to observe similar associations using this phenotype description, albeit with reduced power. To this end, we compared results of multinomial response models of a nominal classification of each tumor to those from the multivariate response models of the subtype signatures. We binned the tumors by assigning each to the cluster with the largest signature and used polytomous regression analysis to measure associations between the resulting designations and selected variables, including age and race in all samples and for family history among advanced stage, high-grade serous samples. While the associations observed in the multinomial models were non-significant, the directions and relative magnitudes of the individual effects were comparable as was expected.
We also examined relationships between the tumor and epidemiologic risk factors and the TCGA survival signature. Although this signature was derived from serous tumors, our findings suggest that the TCGA survival signature is even more consistent with other histologic subtypes compared to the serous cancers. Compared to a poorly differentiated cancer, having a well to moderately-differentiated cancer was inversely associated suggesting molecular changes in the survival signature are the underlying components of tumor grade. A possible interpretation of our results is that these other prognostic factors are independent of the TCGA signature in the predication of survival/outcome. Further work is required to understand the implications of these findings, if confirmed.
Our results are consistent with the previous reports of the association of the molecular signatures with histology, grade and stage. Although we report novel findings of relationships of tumor and epidemiologic characteristics indicative of heterogeneity between molecular signatures, the current study has several notable limitations. Due to the relatively small sample size and inherent uncertainty in defining the signatures in FFPE tissue, the power to detect associations is not optimal. Since we used existing characterization of the molecular signatures, derived mostly from the serous and endometrioid histologic subtypes, patient survival, and among mostly Caucasian subjects, it is possible that these previously defined signatures may not be indicative of etiologic processes or pathways or perhaps not representative of molecular signatures in non-white populations. A more direct approach for detecting molecular signatures indicative of etiologic pathways and those that represent other racial/ethnic groups would be to conduct a supervised analysis to determine whether molecular signatures can be found that are associated with exposure to risk factors, an important future direction of study. Only recently has the technology advanced to make this feasible in a population-based study where FFPE tissue would be necessary to use.
Another limitation of this study is that we were unable to conduct a systematic evaluation of our findings in an independent dataset as we were unable to locate a study that paired the necessary epidemiological risk factor with tumor assay data. However, we were able to evaluate associations between the subtype signatures and race, family history of breast or ovarian cancer (using BRCA1/2 germline mutation status as proxy) and age at diagnosis, using the recently published TCGA data (4) and found the same pattern of association that we observed with age at diagnosis and family history in the NCOCS data. Younger age at diagnosis was associated with the immunoreactive, the well differentiated and the low stromal response signatures in both the TCGA data and the NCOCS data, while an older age at diagnosis was associated with the mecsenchymal/differentiated signature in both datasets. Those who reported a family history of breast or ovarian cancer in a first degree relative were more likely to be classified as having a high stromal content or immune reactive (C1 or C2). Ultimately, a validation study with a much larger sample size will be necessary to adjudicate the associations we observe in the current study.
In summary, this is the first report to investigate whether ovarian cancer risk factors are differentially associated with existing definitions of molecular signatures of epithelial ovarian cancer. Although we report novel findings, further work is needed to determine whether our results can be confirmed and whether a different approach to define clusters of molecular expression in tumor tissue may be more optimal for discerning subtypes associated with the pathogenesis of ovarian cancer.
Supplementary Material
Acknowledgements
American Cancer Society, Barbara Thomason Research Professorship
Grant Support This research was supported by an NIH grant number R01 CA176016 (JMS) and UO1 CA084955 (JRM).
Footnotes
Conflicts of interest: none
Disclosure of Potential Conflicts of Interest No potential conflicts of interest were disclosed.
Authors’ Contributions Conception and design: Joellen Schildkraut, Andrew Berchuck
Development of methodology: Edwin Iversen
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Joellen Schildkraut
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Joellen Schildkraut, Lucy Akushevich, Edwin Iversen,
Writing, review, and/or revision of the manuscript: Joellen Schildkraut, Edwin Iversen, Jeffrey Marks, Andrew Berchuck, Lucy Akushevich, Rex Bentley
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Lucy Akushevich, Regina Whitaker, Edwin Iversen
Study supervision: Joellen Schildkraut
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