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. 2019 Apr 19;3:CCI.18.00096. doi: 10.1200/CCI.18.00096

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma

Arunima Shilpi 1, Manoj Kandpal 1, Yanrong Ji 1, Brandon L Seagle 1, Shohreh Shahabi 1, Ramana V Davuluri 1,✉
PMCID: PMC6873993  PMID: 31002564

Abstract

PURPOSE

Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression–based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC).

METHODS

Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets.

RESULTS

Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant (P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms.

CONCLUSION

Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

INTRODUCTION

High-grade serous ovarian carcinoma (HGSOC) accounts for 70% to 80% of ovarian cancer deaths, with little improvement in overall survival (OS) in recent years.1 The standard therapy for HGSOC includes maximal cytoreductive surgery followed by platinum and taxane chemotherapy. Although the majority of patients with HGSOC respond to initial treatment, most tumors recur and become increasingly resistant to chemotherapy, with a 5-year OS rate of approximately 30%.2 As a heterogeneous disease, HGSOC molecular subtyping can serve as a useful clinical tool to predict response to therapy and inspire novel personalized medicine treatment plans. Indeed, genomic and transcriptome profiling by The Cancer Genome Atlas (TCGA) consortium and others revealed few recurrent somatic mutations but a highly complex genomic terrain marked by copy number alterations and intertumor heterogeneity.3 Four molecular subtypes—mesenchymal (M), immunoreactive (I), differentiated (D), and proliferative (P)—were independently identified, first by the Australian Ovarian Cancer Study (AOCS)4 and then by the TCGA consortium.3 However, TCGA subtypes did not show statistically significant survival differences. Therefore, an important question is whether the unsupervised clustering of samples on the basis of gene-level annotations is still the best approach to identify clinically relevant molecular subtypes.

CONTEXT

  • Key Objective

  • To develop a novel platform-independent molecular subtyping assay for high-grade serous ovarian carcinoma classification.

  • Knowledge Generated

  • Refined molecular subtyping on the basis of the isoform-level transcriptome of The Cancer Genome Atlas high-grade serous ovarian carcinoma cohort switched the subtype calls for 22% of the samples and led to improved prognostic stratification among the subtypes. The derived gene/isoform-level signatures are platform independent such that more than 90% of the patients were correctly classified into one of the four molecular subtypes, irrespective of whether the gene expression data were generated by either a microarray or a next-generation sequencing platform.

  • Relevance

  • We anticipate that the molecular subtyping assay can be used in a next-generation sequencing clinical diagnostics laboratory in combination with pathologic information, advanced imaging analytics, and radiomics approaches to enable improved precision diagnostics and treatment planning.

The majority of human genes produce multiple functional products, or isoforms, through alternative transcription and alternative splicing.5-7 Different protein isoforms of a gene participate in different functional pathways,8,9 and cancer-associated aberrant alternative splicing events have been reported.10-13 In ovarian cancer, specific splice variants have been identified as prognostic markers and predictors of resistance to therapy, such as p53δ of TP53 and CD44v8-10 of CD44 as prognostic markers14,15; MRP1 splice variant resistance to doxorubicin16; the role of EVI1 transcript variant (EVI1Del190-515) in tumorigenesis17; and the role of osteopontin-c isoform in active proliferation, migration, and tumor growth.18 Therefore, specific transcript variants could be more effective as diagnostic and prognostic markers than corresponding genes19,20 and suggest that biomarker and molecular subtyping studies should explore the isoform-level transcriptome. We hypothesized that isoform-based clustering of HGSOC tumors will lead to more clinically relevant subgrouping than gene-level subgrouping. Moreover, isoform-level gene classifiers can identify specific isoforms as biomarkers and generate robust and clinically translatable assays for HGSOC stratification. We have adopted our previously developed classification system, PIGExClass (platform-independent isoform-level gene-expression based classification-system) to robustly cluster HGSOC tumors on the basis of isoform-level transcriptome profiles, develop platform-independent classification models for HGSOC molecular subtyping, and validate the classifiers on independent data sets from different platforms.

METHODS

Preprocessing of TCGA HGSOC Exon Array Data

TCGA unprocessed exon array data for 569 HGSOC and eight normal samples were downloaded from the Genomic Data Commons portal.21 Gene-level and isoform-level expression estimates were obtained using multimapping Bayesian gene expression for whole-transcript arrays,22 using Ensembl database version 56 as the reference genome. Expression estimates were normalized across the samples using the LOWESS algorithm.23

Data Filtration

Two-step filtration was applied to obtain highly variable isoforms for clustering. The first filter retains only one isoform among highly correlated isoforms of the same gene. Two isoforms of a gene are considered highly correlated if the Pearson’s correlation coefficient of isoform-level expressions across the samples is higher than 0.8. The isoform with the highest coefficient of variation was retained among the correlated isoforms of a gene. The second filter selects, using coefficient of variation, the isoforms that are most variable across patients.

Identification of HGSOC Subtypes on the Basis of Isoform-Level Expression

Unsupervised non-negative matrix factorization consensus clustering was applied using the NMF package of R.24-26 Consensus matrices with different factorization ranks (2 to 7) were obtained by taking the average of 50 connectivities. Clustering quality was evaluated using the cophenetic correlation coefficient and heat map plots (Data Supplement). The factorization was repeated for 100 runs, and the one with the lowest approximation error was retained. Samples that were not true representatives of the subclasses were filtered out by silhouette width procedure.27

Identification of OS Differences Among the Subtypes

Log-rank test was applied to determine the prognostic relevance of the four subtypes. Kaplan-Meier survival curves were plotted using the R function survival.28 Pairwise comparison of survival among subtypes was adjusted for multiple comparisons using the Benjamini-Hochberg procedure.29 Imputation of missing values was performed by the R package mice.30 The association of the subgroups with OS was modeled by fitting a multivariable Cox proportional hazards (PH) regression model adjusted for age, tumor stage, cytoreduction, and chemotherapy.

Processing of the RNA Sequencing Data Set

TCGA RNA sequencing (RNA-seq) data for 376 HGSOC samples were downloaded from the Genomic Data Commons portal.21 A subset of 279 samples were profiled by both exon array and RNA-seq platforms. For control samples, RNA-seq data for six normal fallopian-tissue samples were downloaded from the Genotype-Tissue Expression database. RNA-seq data were analyzed using Picard tools31 and the RNA-Seq by Expectation-Maximization32 program.

Variable Selection and Building Classification Model

We adopted the PIGExClass algorithm, which combines data discretization and random forest–based variable selection procedures, to build gene/isoform-level classifiers.33 Equal-frequency binning data discretization was applied on fold-change values (cancer over normal).34 Variable selection was performed using fold-change estimates to select a small set of nonredundant genes/isoforms that were used to build random forest–based classification models. Tenfold cross-validation was applied followed by training the models on 75% of the samples and testing on the remaining 25% of samples.

Validation of Classification Model on Independent Data Sets

We evaluated the gene-level classification model on data from two independent platforms. The misclassification rate was computed for 279 RNA-seq samples that were profiled by both exon array and RNA-seq platforms. In addition, we built gene-level classification models on the TCGA exon array data set and validated the models using two independent microarray data sets—GSE98914 and GSE2671235—downloaded from the Gene Expression Omnibus database.36 This study followed the recommendations in the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement (Data Supplement). Additional details about the validation methods are provided in the Data Supplement.

RESULTS

Numerous Genes Were Differentially Expressed at the Isoform Level Between HGSOC and Normal Samples

We obtained expression estimates for 35,612 gene-level and 114,930 transcript-level features by analyzing the TCGA exon array data set of 569 tumor and eight normal samples. Although only 1,634 genes were obtained as differentially expressed at the gene level, isoform-level analysis resulted in 4,723 transcript variants corresponding to 2,245 genes as differentially expressed between HGSOC and normal samples (fold-change ≥ 2; q ≤ .001; Table 1). Because we found more than double the number of genes differentially expressed at the isoform level than at the gene level, we investigated whether the isoform-level transcriptome can provide a better molecular subgrouping of patients with HGSOC in terms of overall prognosis.

TABLE 1.

Number of Up- and Downregulated Genes or Transcript Variants Identified in the TCGA Ovarian Cohort Exon Array Data

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Unsupervised Clustering of TCGA HGSOC Samples Using Isoform-Level Transcriptome Recaptured the Molecular Subtypes With Improved Prognostic Stratification

The TCGA Research Network reported four gene-based molecular subtypes with no significant survival differences (P = .117; Data Supplement). Therefore, we performed clustering of 569 TCGA HGSOC samples using isoform-level expression estimates of 930 highly variable isoforms. The samples clustered into four distinct subgroups, which largely overlapped with the TCGA subgroups and, as such, refined the TCGA subtypes. Therefore, we retained TCGA subgroup nomenclature of I, D, P, and M on the basis of the concordance in cluster membership calls between our isoform-based groups and the TCGA core sample subgrouping (Fig 1A).

FIG 1.

FIG 1.

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(A) Unsupervised non-negative matrix factorization clustering of 569 patients with high-grade serous ovarian carcinoma (HGSOC) on the basis of highly variable (930) isoform-level signatures. The clusters were identified and assigned into four subgroups on the basis of original The Cancer Genome Atlas core grouping. The color code for samples in each cluster are as follows: differentiated (D), black; immunoreactive (I), red; mesenchymal (M), pink; and proliferative (P), blue. (B) The concordance table shows the agreement of sample assignment with The Cancer Genome Atlas subgroups (gene-based subtypes) and our isoform-based subgroups. Although the P subgroup showed good agreement, the I subgroup showed the worst agreement followed by the M subgroup. (C and D) Kaplan-Meier survival curves plotted to determine the prognostic difference among four the subtypes identified using gene- and isoform-level expression-based clustering, respectively. The statistical significance in overall survival was determined at a threshold P = .05.

To find homogenous clusters, we filtered out 134 samples with negative silhouette width values, which resulted in the final set of 435 samples (isoform-based core samples) grouped into 128 as I, 106 as D, 97 as M, and 104 as P. Among the 371 tumor samples that are common between TCGA and our isoform-based subgroups, on the basis of the concordance table (Fig 1B), 80 samples were clustered into a different subgroup by our isoform-based clustering. The switching of 22% of the samples into a different subgroup resulted in a statistically significant difference in OS among the four isoform-based subtypes (P = .006; Fig 1D), whereas, the gene-based subtypes, derived by using the gene-level expression profiles, did not show significant OS differences (P = .057; Fig 1C), similar to the TCGA subtypes (P = .117; Data Supplement). In pairwise comparisons among the four subtypes, after controlling for false discovery rate, we found that isoform-based subtype pairs D and M (P = .0145) and I and M (P = .0332) were significantly different in OS (Fig 1D). However, none of the adjusted pairwise comparisons among TCGA subtypes were statistically significant at the α = .05 level (Data Supplement). Similarly, for the gene-level subtyping of 569 samples (Fig 1C), although the OS P value was smaller compared with that of TCGA subgrouping (Data Supplement), it was still not significant at α = .05. For pairwise comparisons, only one comparison (D v M) was found to be statistically significant. For the isoform-based subgroups, the median OS for the M subtype was 3.2 years (95% CI, 2.6 to 4 years); for the D subtype, 4.2 years (95% CI, 3.8 to 4.8 years); for the I subtype, 4 years (95% CI, 3.0 to 6.8 years); and for the P subtype, 3.6 years (95% CI, 3.2 to 4.1 years).

Multivariable Cox PH regression analysis revealed that isoform-based, but not gene-based, molecular subtypes have a significant association with OS after accounting for clinical variables age, tumor stage, residual tumor size after cytoreductive surgery, and chemotherapy (intravenous or intraperitoneal). More specifically, the I subtype hazard ratio was 0.66 (95% CI, 0.45 to 0.97; P = .034) compared with that of the M subtype and after accounting for the four clinical variables. However, although the D and P subtypes showed better OS compared with the M subtype, the Cox PH P values were not statistically significant at the α = .05 level (Table 2). Although factors such as increased age at the time of diagnosis, tumor stage III or IV, and suboptimal debulking surgery (residual disease > 0 cm) are associated with an increased risk of death, intraperitoneal chemotherapy was associated with longer survival than intravenous chemotherapy (Table 2). In summary, both univariable and multivariable survival analyses demonstrated that the isoform-level subgrouping shows improved association with OS compared with the gene-based subgrouping.

TABLE 2.

Cox Proportional Hazards Regression Model by Gene- and Isoform-Based Subgroups

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Gene- and Isoform-Based Classification Models for HGSOC Subtype Prediction

Using the isoform-based subtypes as the four classes, we built both gene-based and isoform-based classifiers on the basis of the 435 isoform-based core samples. Although the gene-based classifier can be applied to data from either microarray or RNA-seq platforms, the isoform-based classifier is mainly for exon array and RNA-seq platforms. The random forest feature selection step selected 206 isoforms as feature variables that are most discriminating among the four subgroups (Fig 2A; Data Supplement). The isoform-based random forest classifier achieved 92% accuracy with 206 isoforms as feature variables when trained and tested by 10-fold cross-validation on the TCGA exon array data set. The classifier was further tested by dividing the isoform-based core samples into two groups: 75% of samples to be used as a training set and 25% to be used as a test set. The classification model generated from the training set was applied to the test set. The results of this additional testing agreed with those of the 10-fold cross-validation approach in 99% of the sample calls in the test set, which confirmed that the algorithm effectively distinguishes the four subgroups. Similarly, the gene-based classifier achieved 91% accuracy, with 132 genes as feature variables (Fig 2B; Data Supplement). Although the feature selection step of the random forest algorithm selected a different number of features for gene- and isoform-based classifiers, both classifiers achieved more than 90% accuracy with as few as 100 feature variables (Fig 2).

FIG 2.

FIG 2.

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The classifier was generated on the basis of the selection subset of (A) isoform/transcript variants and (B) genes. Out-of-bag (OOB) error rate was plotted, where the x-axis denotes the selection of features/variables and the y-axis represents the error rate.

Platform Transition of the Classification Models to RNA-Seq and Other Microarray Platforms

Because the gene- and isoform-based classifiers (both of which were derived on data from the exon array platform) achieved a prediction accuracy of more than 90%, we tested the robustness and generalizability of the classification models by testing on data from independent platforms, such as RNA-seq and other microarray platforms. First, we evaluated the transition of the isoform-based classifier from the exon array to the RNA-seq platform by applying 279 RNA-seq TCGA samples that overlapped with the isoform-level core samples and were profiled by both exon array and RNA-seq methods. Therefore, the class labels for these 279 samples are known from the isoform-level clustering. By comparing the concordance among the prediction calls by applying the classifier on RNA-seq data and true-class labels, we found that the classifier made 95% (Table 3; Data Supplement) similar subtype calls between the two platforms and achieved 91% prediction accuracy compared with the true-class labels (Data Supplement).

TABLE 3.

Concordance Between the True-Class Labels and Predicted Calls by the Isoform-Level Classifier That Was Trained on the Exon Array Data Set and Applied on RNA-Seq Data Set

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Next, we tested the gene-based classification models derived from the TCGA exon array data set on data from two independent studies.4,35 After filtering outlier genes (not well-behaved genes) between the exon array and other microarray platforms by fitting regression models between the mean gene expression vectors from the exon array and microarray platforms, we retained 11,319 and 7,280 genes from the GSE9891 and GSE26712 data sets, respectively. Two separate classifiers that consisted of 106 and 43 variables were derived for the GSE9891 and GS26712 data sets, respectively (Data Supplement). Because the true accuracy of the classifiers cannot be assessed as a result of the nonavailability of the class labels in these two data sets, we evaluated the degree of concordance between the previously defined subgroups and the subtypes predicted by our classifiers. We compared the predicted molecular subtypes with those of previously predicted molecular subgroupings (C1 [high stromal response], C2 [high immune signature], C4 [low stromal response], and C5 [low immune signature] for HGSOC) by AOCS.4 Overall, we found that the isoform-level subtyping was significantly associated with the AOCS study subtyping (χ2 test P < .001), where the C1, C2, C4, and C5 subgroups mapped to the M, I, D, and P isoform-based subgroups, respectively (Table 4). Within the combined data set of 430 patients, the predicted subgroups showed a significant difference in OS (P < .001). Moreover, the OS pattern of the predicted subgroups mirrored that of our isoform-based subgrouping, and the patients grouped under the M class were found to have the highest risk of death, with a median survival of 2.25 years (Fig 3).

TABLE 4.

Overlap in Cluster Membership of 245 Ovarian Serous Samples Between Our Predicted Isoform-Based Subgroups and AOCS Clusters (Gene Expression Omnibus GSE9891)

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FIG 3.

FIG 3.

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The prediction of four classes in 378 high-grade serous ovarian carcinoma samples obtained from Gene Expression Omnibus (GSE9891 and GSE26712) data set was clinically evaluated for prognostic determinates. Kaplan-Meir survival plot of the four subtypes shows significant difference in overall survival rate (P < .001).

DISCUSSION

Molecular classification of cancers is essential for developing personalized therapies.37 Although gene expression–based patient stratification strategies have been published for numerous cancer studies, the reproduction and validation of the derived gene signatures across different laboratories or profiling platforms have proven to be a complex and difficult informatics problem. In classification, the main goal is to derive a probabilistic model for predicting the class membership of a new observation (patient with ovarian cancer) on the basis of a training set of data (tumor samples) that contains observations (eg, gene expression measures) for which class membership is already known. A considerable overlap of the molecular subtypes exists among various HGSOC publications,3,4,38,39 but these studies were solely based on gene-level expression estimates and ignored the variability associated with splice/transcript variants. Although aberrant expression of splice variants in ovarian cancer has been reported,40-42 the current study is the first to our knowledge that has explored the use of isoform-level transcriptome in the molecular subtyping of ovarian cancer. Our isoform-level subtypes provide more prognostic information than the gene-level subtypes that were previously reported.3,4,35,43 Similar overall prognostic significance among the four molecular subtypes have been reported,39 but we noticed some inconsistencies between independent data sets. For example, we observed similar overall prognostic significance and pairwise (D v M, I v M) survival differences in both TCGA (Fig 1D) and independent data sets (Fig 3). However, the survival difference between the I and M subtypes was inconsistent between the TCGA and the other two cohorts, which could be due to differences in the patient population and errors in the subtype calls on data from different platforms. Although the univariable Kaplan-Meier survival analysis showed both D and I isoform-based subtypes as significantly different in their OS from the M subtype, multivariable Cox PH regression analysis showed only the I subtype as significantly different from the M subtype after adjusting for the clinical variables.

Having established improved association of the refined subgroups with the OS, we translated the exon array–based classification system to independent platforms. The testing of the derived models on data from two independent platforms showed a high level of cross-platform (exon array to RNA-seq) consistency and accuracy (> 90%) without loss of analytic precision. In addition, the classification system simplified the interplatform translation with the selection of a small subset of genes and/or transcript variants as feature variables. The small set of genes/transcript variants for molecular classification will be clinically useful and cost effective for patient subgroupings and a key resource to develop precision medicine strategies further.43 For example, molecular subtypes with poor prognoses (P and M subgroups) have been reported to benefit from treatment with the vascular endothelial growth factor inhibitor bevacizumab.44,45 Furthermore, patients in the P subgroup are sensitive to poly (ADP-ribose) polymerase inhibition (veliparib).43,46 These data provide a rational basis for selecting specific treatments for histologic and molecular subtypes of ovarian cancer. A major limitation of the classification system is the requirement of the tumor transcriptome profile from either a microarray or a next-generation sequencing platform. To translate the assay to a low-dimensional platform, such as quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) or NanoString (NanoString Technologies, Seattle, WA), additional experiments are required on an independent patient cohort.33 In addition, the combining of the molecular subtype information with pathologic subtypes, advanced imaging analytics, and radiomics approaches would enable improved precision diagnostics and treatment planning.47,48

In conclusion, we developed a new platform-independent isoform-level classification system for efficient and accurate stratification of patients with HGSOC with prognostic significance. The classifiers derived here have the potential to develop into prognostic biomarkers for stratification of patients with HGSOC.

ACKNOWLEDGMENT

We thank W.S. Dhiman, PhD, and A.K. Grace, PhD, for their scientific review of the manuscript.

Footnotes

Presented at the American Association for Cancer Research Addressing Critical Questions in Ovarian Cancer Research and Treatment, Pittsburgh, PA, October 1-4, 2017.

Supported by the National Library of Medicine of the National Institutes of Health (R01LM011297; R.V.D.) and partially supported by the Phebe Novakovic Fund and the John and Ruth Brewer Endowment.

AUTHOR CONTRIBUTIONS

Conception and design: Shohreh Shahabi, Ramana V. Davuluri

Financial support: Shohreh Shahabi, Ramana V. Davuluri

Administrative support: Shohreh Shahabi, Ramana V. Davuluri

Collection and assembly of data: Arunima Shilpi, Manoj Kandpal

Data analysis and interpretation: Arunima Shilpi, Manoj Kandpal, Brandon L. Seagle, Yanrong Ji, Shohreh Shahabi, Ramana V. Davuluri

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc.

Shohreh Shahabi

Research Funding: AbbVie (Inst)

Ramana V. Davuluri

Patents, Royalties, Other Intellectual Property: Patent US10113201B2: Methods and Compositions for Diagnosis of Glioblastoma or a Subtype Thereof (Inst)

No other potential conflicts of interest were reported.

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