Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian cancer (HGSOC) is the most common histologic subtype of ovarian cancer, which accounts for 70%–80% of ovarian cancer–related deaths, and is associated with poor prognosis and frequent relapse (1). HGSOC is a genomically complex disease that is characterized by ubiquitous TP53 mutations (2), frequent loss of RB1, NF1, and PTEN by gene breakage events (3, 4), and recurrent high-level copy-number (CN) amplifications (5).

Molecular stratification of HGSOC is difficult due to the genomic complexity and extensive heterogeneity of the disease. Clinically relevant genomic stratification is currently restricted to the identification of homologous recombination deficiency (HRD), a condition that is present in roughly half of all HGS ovarian tumors, and that is attributable to germline or somatic BRCA mutations in approximately 20% of HGSOC cases (6, 7).

Several studies also reported molecularly distinct subtypes by clustering tumors together that have similar transcriptome profiles (3, 8–10). The Cancer Genome Atlas (TCGA) project reported four subtypes (3) and named them on the basis of marker gene expression: differentiated (DIF), immunoreactive (IMR), mesenchymal (MES), and proliferative (PRO). These subtypes were found associated with several clinical and tumor pathology characteristics (3, 9, 11), potentially reflect different tissues of origin (12), and partly reflect differences in immune cell (13) and stromal (14) content. Robustness and clinical utility of the transcriptome subtypes remain controversial (15, 16). On the basis of a compendium of 15 microarray datasets consisting of |{\rm{approximately}} $| 1,800 HGSOC tumors, subtype classifiers were not robust to refitting in independent datasets and grouped only one-third of patients concordantly into four subtypes (11).

Most HGSOC tumors are polyclonal, meaning that a single tumor is a heterogeneous assembly of distinct cancer genotypes arising from different subclones. Lohr and colleagues estimated that 95% of TCGA HGSOC tumors are polyclonal, and |{\rm{approximately}}\ $| 40% consist of ≥4 subclones (17). Recent single-cell studies further demonstrated extensive intratumoral heterogeneity of HGS ovarian tumors (18–20), consistent with the notion of an individual tumor being an intermixture of different malignant cell populations. Transcriptome subtypes might provide a useful summary of bulk tumor properties if these subtypes are shared by different intratumoral subclones, representing an intrinsic difference between tumors. However, we hypothesized that subtype assignment via transcriptome clustering is driven by late events in tumor evolution so that these subtype properties would be subclone specific. We tested this hypothesis first through a novel use of allele-specific copy number of genomic regions identified as subtype markers in bulk tumors. In contrast to phylogenetic analysis based on longitudinal whole-genome sequencing at multiple timepoints (21, 22), the proposed approach infers tumor evolution from copy-number data obtained with genotyping arrays or exome sequencing at a single timepoint. We followed up with single-cell RNA-sequencing (scRNA-seq) analysis of 6 independent patients and investigated whether individual epithelial cancer cells have different well-defined subtypes and whether they correspond to the proposed subtypes of bulk tumors.

Statistical analysis was carried out using R (23) and Bioconductor (24). Code is available from GitHub (https://github.com/waldronlab/subtypeHeterogeneity).

All single-cell patient samples were collected using protocols approved by the University of Minnesota's Institutional Review Board (Minneapolis, MN, protocol numbers 1408M52905 and 1611M99903). Controlled access TCGA data (DNA sequencing BAM files) were downloaded from the Genomic Data Commons using a key obtained through dbGaP (dataset phs000178.v10.p8.c1, data access request #47794-4), and handled in accordance with the General Research Use requirements for use of this dataset.

Somatic copy-number alterations (SCNA) detected with GISTIC2 (25) were obtained from the final run of the TCGA Firehose pipeline (2016-01-28). SCNAs were classified depending on their type (deletion/amplification) as either normal, loss/gain of a single copy, or loss/gain of two or more copies. Transcriptome clusters, assigning each tumor to a subtype (3), were retrieved from the 2016-01-28 Firehose run. Subtype association was tested by |{\chi ^2}$| test with |df\ = \ 6$|⁠. Multiple testing correction was carried out using an FDR (26) cutoff of 0.1.

ABSOLUTE (27) SCNA calls were obtained from the Pan-Cancer Atlas aneuploidy study (28). This included per-tumor estimates of purity, ploidy, subclonal genome fraction, number of genome doublings, and segmented absolute CN calls classified as occurring clonally or subclonally. ABSOLUTE calls were managed in the R/Bioconductor data class RaggedExperiment, which facilitated summarization of ABSOLUTE's subclonality calls in GISTIC2 regions using the qreduceAssay function. A region was called subclonal if it was overlapped by at least one subclonality call. GISTIC2 peaks were extended by 500 kb up- and downstream to account for uncertainty of the peak calling heuristic.

Using the |{\chi ^2}$| test statistic as the subtype association score, |{S_A},$| of an alteration, and the fraction of tumors for which this alteration was subclonal as the subclonality score, |{S_C}$|⁠, Spearman rank correlation was computed to assess the relationship between |{S_A}$| and |{S_C}$|⁠. Statistical significance of the correlation was assessed using Spearman rank correlation test. To account for nonindependence of the occurrence of different SCNAs, we also carried out a permutation test, where we permuted the observed |{S_A}$| values 1,000 times and recalculated the correlation with the observed |{S_C}$| values. The |P\ $|value was obtained by calculating the fraction of permutations in which the correlation of the permuted setup exceeded the observed correlation.

Subtypes were classified using the consensus classifier implemented in the function get.consensus.subtypes of the consensusOV Bioconductor package (11). The consensus classifier calculates a score [more precisely: a probability in (0,1)] for each subtype and assigns each tumor to the subtype with the highest score. A tumor with a large difference or margin between the highest and second highest scores can be considered confidently classifiable, whereas a tumor with two nearly equal scores can be considered of ambiguous subtype.

Five additional HGSOC specimens were subjected to 10× Genomics scRNA-seq, producing an average of 235 million reads per patient sample with an average of 11,502 cells/sample and 23,000 reads/cell. Statistical analysis was carried out using a collection of Bioconductor packages for single-cell analysis (29). Sample collection, experimental procedures, and data processing steps are described in Supplementary Materials and Methods S1. Raw and processed scRNA-seq data are available from NCBI's Gene Expression Omnibus database (accession number: GSE154600).

We previously reported a systematic assessment of four reported HGSOC subtypes with respect to robustness and association to overall survival, and found that most tumors cannot be classified reliably, but that it is possible to predict how reliably each tumor can be classified and that some tumors can be classified with high confidence (11). Here, we investigate an alternative model of HGSOC development, in which ambiguity in tumor classification arises as a consequence of accumulated mutations and intratumor heterogeneity (Fig. 1). To test this hypothesis, we focus on SCNAs given their causal roles in oncogenesis and their potential to discriminate between cancer subtypes (30). The GISTIC2 method detects SCNAs that are more recurrent than expected by chance, to distinguish cancer-driving events from random passenger alterations (25). The method also separates broad arm-level events from narrow focal events, which often harbor oncogenes and tumor suppressors (25). The ABSOLUTE algorithm (27) infers tumor purity and ploidy from the analysis of SCNAs, and incorporates this information for quantification of an alteration's absolute CN per cancer cell. The algorithm also identifies SCNAs not fitting a tumor's purity and ploidy relationship as a consequence of subclonal evolution.

Focusing on the subtypes proposed by TCGA (3), we integrated information from 516 TCGA cases GISTIC2 and ABSOLUTE SCNA calls to analyze whether recurrent subtype-associated SCNAs display greater intratumor heterogeneity than other alterations. We assessed the reliability of these calls by absolute CN analysis on whole-exome sequencing data, and complemented results with single-cell subtype classification on approximately 42,000 cells from six independent tumors.

Previous studies reported specific clinical and tumor pathology characteristics of the four subtypes (3, 11). Using previously published (28), curated allele-specific copy-number estimates obtained by ABSOLUTE analysis, we observed significant differences in tumor purity (defined as the proportion of malignant cells) between subtypes (Supplementary Fig. S1A) in agreement with previous reports (31). Tumors of differentiated subtype were characterized by high purity, but lower ploidy and subclonality than the other three subtypes (Supplementary Fig. S1B and S1C), consistent with a lower number of genome doublings (Supplementary Fig. S1D).

To establish the reliability of inferring SCNA subclonality with ABSOLUTE from SNP array data, we investigated whether results were consistent when using whole-exome sequencing data instead. In a separate benchmarking study (32), we compared results from absolute copy-number analysis of genotyping arrays with matched normal samples (the approach used here) with allele-specific copy-number analysis of exome sequencing with and without matched normal samples, using the PureCN R/Bioconductor package (33). There (32), we report that per-tumor estimates of purity and ploidy were in good agreement between experimental platforms and computational methods (Pearson correlation of 0.77 for purity and 0.74 for ploidy). This also applied to individual CN calls in GISTIC2 regions, where we found a median concordance of 87.7% of tumors with identical CN state for one GISTIC2 region at a time.

We analyzed recurrent focal SCNAs as identified with GISTIC2 in TCGA HGSOC tumors for association with subtypes (Fig. 2). We tested all 70 SCNAs identified by GISTIC2, comprising 31 amplifications and 39 deletions (Fig. 2, outer ring). Nominal P values showed a concentration near zero (Supplementary Fig. S2A and S2B), corresponding to 35 alterations being significantly associated with subtypes (FDR < 1; Fig. 2, inner ring). Associations with the proliferative subtype were significantly overrepresented (20/35, |P = \ 0.007$|⁠, Fisher exact test; Fig. 2, barplot). Strongest subtype association was observed for the FRS2 and BLC2L1 amplifications (Fig. 2, gene names).

We tested the hypothesis that reported HGSOC subtypes differentiate late in tumorigenesis by assessing the correlation between subtype association and subclonality of recurrent SCNAs (Fig. 1A–E). Subtype association of an alteration was calculated via a score, |{S_A}$|⁠, corresponding to the |{\chi ^2}$| test statistic (Fig. 3A). Subclonality prevalence of an alteration was calculated via a score, |{S_C}$|⁠, defined as the fraction of samples for which this alteration was subclonal (Fig. 3B).

Under the null hypothesis that subtype-associated alterations occur no earlier or later than other alterations, Spearman correlation |\rho $| between |{S_A}$| and |{S_C}$| would be expected to be zero:

 Rejection of |{H_0}$| has clear interpretation: if subtype-associated SCNAs tend to be subclonal, that is,

this suggests that the subtypes are late events in tumor evolution. If subtype-associated alterations tend not to be subclonal, that is,

this instead suggests that subtypes are early events, consistent with these being “intrinsic” subtypes.

We obtained a significant positive correlation between subtype association and subclonality prevalence of the 70 SCNAs (Fig. 3C). To account for nonindependence of the occurrence of different SCNAs, we carried out a permutation test (⁠|P = \ 0.006$|⁠). When stratifying tumors by purity, because very high or low purity tumors present challenges for allele-specific copy-number analysis, the correlation was positive in all strata and did not significantly differ between strata (Supplementary Fig. S3A and S3B). Highest subtype association and subclonality were observed for amplifications, and repeatedly displayed increased alteration frequency for the proliferative subtype (including amplification of BRD4 and the telomeric 20q13.33; Supplementary Figs. S4 and S5). A notable exception was the highly subclonal MYC amplification, which displayed decreased alteration frequency for proliferative tumors as reported previously (3). Predominantly clonal alterations were enriched for deletions (9/10 regions with |{S_C}\ \lt \ 0.3$|⁠) with moderate subtype association (including loss of PPP2R2A and MGA; Supplementary Fig. S4). Consistent with the reported frequent loss of PTEN, RB1, and NF1 in HGSOC (3, 7), these alterations occurred predominantly clonally and largely irrespective of subtype classification (Supplementary Fig. S6).

As an opposing example, we repeated the analysis for adult soft-tissue sarcoma (STS), which, like HGSOC, is characterized by high SCNA frequency; however, unlike HGSOC, TCGA STSs are comprised of sarcoma types from different anatomic sites that are characterized by distinct genomic alterations, consistent with them being true intrinsic subtypes (34). In contrast with HGSOC, association with the STS type–dominated transcriptome clusters was negatively correlated with subclonality prevalence (Fig. 3D; Supplementary Results S2.1), consistent with STS being characterized by early/intrinsic type-specific events (34).

We assessed whether subtype classification ambiguity exists at the level of single cells, or arises from individual epithelial cells of the same tumor being classified as different subtypes (Fig. 1F). We analyzed data from Fluidigm deep sequencing of 66 cells from a previously published tumor (18), and 10× Genomics shallow sequencing of 42,000 cells from five additional tumors. We applied the consensusOV subtype classifier, previously trained on tumors concordantly classified by three major subtype classifiers across 15 microarray datasets (11), to single cells in conjunction with cell type classification. The consensusOV classifier also displayed high concordance when classifying TCGA tumors assayed by bulk RNA-seq and microarray (Supplementary Fig. S7A).

From application of the consensus classifier to bulk RNA-seq and Fluidigm scRNA-seq of 66 cells from a previously published tumor (18), we observed higher subtype ambiguity for single cells when compared with the bulk tumor (Supplementary Results S2.2, Supplementary Figs. S7 and S8). The observed ambiguity in classification of single cells could not be attributed to the low coverage of scRNA-seq data (35), as downsampling of bulk RNA-seq data to match the coverage of the scRNA-seq data still allowed for a more confident subtype assignment (Supplementary Fig. S7B). This also demonstrated new challenges for subtype classification of single cells, as many cells substantially expressed only small parts of the subtype signatures derived from bulk tumors (Supplementary Fig. S7C).

To investigate whether these observations hold when analyzing the full complement of tumor cells on a larger scale, we employed 10× Genomics scRNA-seq for five additional tumors. The tumors were clinically classified as stage III–IV, grade 3, and displayed different responses to chemotherapy (Supplementary Table S1). After quality control, the number of cells analyzed for each tumor ranged from 3,630 to 13,747 cells, comprising a total of 42,253 individual tumor cells.

Cell type annotation via marker gene expression and transcriptome similarity to pure cell types (Supplementary Figs. S9A and S9B, S10, and S11) consistently returned five major cell type populations: epithelial, endothelial, lymphocyte, myeloid, and stromal (Fig. 4B and D). However, the respective proportion of each cell type differed significantly between tumors (Fig. 4D; proportion test, df = 4; P < 2.2 |\times $| 10⁻¹⁶). Noting that a dedicated single-cell classifier should simultaneously classify cell type and subtype of cancerous epithelial cells, we compared cell type assignments with subtype calls of the consensusOV classifier. This demonstrated that certain subtype calls tend to coincide with specific cell types (Fig. 4A, C, and E). This was evident for immunoreactive calls, found on myeloid innate immune cells (79.5%), and mesenchymal calls that coincided with stromal cells (93.2%; Fig. 4E). Epithelial cells were classified as differentiated (84.8%) and to a lesser extent proliferative (13.7%). The cellular tumor composition was reflected in the subtype classification of bulk RNA-seq data for the five tumors (Fig. 4F, top). Tumor T59 was classified as immunoreactive, consistent with large immune cell populations (Fig. 4A and B). Tumor T77 was classified borderline differentiated (DIF)/proliferative (PRO) in agreement with two large DIF/PRO epithelial cell populations, and tumor T90 was classified as proliferative. Tumors T76 and T89 were confidently classified as MES and IMR, recapturing large stromal and immune cell populations. Comparing the margin scores (differences between the probabilities of most-probable and second most-probable subtype, representing certainty of classification) of tumor epithelial cells with the bulk tumor (Fig. 4F, bottom) demonstrated that classification of individual cells was at least as uncertain as for the bulk, consistent with the observations made for the first tumor (Supplementary Fig. S7B). Subtype calls on epithelial cells of tumors T59 and T77 displayed transitional patterns between differentiated and proliferative (Supplementary Fig. S12), similar to the transitional pattern between differentiated and immunoreactive for the first tumor (Supplementary Fig. S7C).

As epithelial cells were classified as either differentiated or proliferative, we investigated which factors drive subtype classification in these two groups of cells by analyzing them for differences in copy-number profile, cell-cycle activity, and expression of bulk subtype markers (Fig. 5). Copy-number profiles inferred from the scRNA-seq data using inferCNV (36) demonstrated genetic heterogeneity between tumors, each with an individual mutational landscape shaped by recurrent bulk SCNAs that were also present on single-cell level (Fig. 5A; Supplementary Figs. S13A and S13B, S14A and S14B, and S15). The small epithelial cell populations of tumors T76 and T89 had relatively few copy-number alterations: T76 was dominated by two arm-level amplifications on chromosome 8 and 20; T89 carried a PAX8 amplification that was not seen in the other tumors. The vast majority of epithelial cells of both tumors were confidently classified as differentiated (mean margin of 0.47 and 0.31, respectively). Tumor T59 showed characteristic indicators of HRD, including strong amplifications of MECOM, PRIM2, and MYC, and loss of RB1 (6, 7). T59 further had two pronouncedly different epithelial cell populations: (i) a larger population comprising approximately 90% of the cells, in which the majority of cells were more confidently classified as differentiated (mean margin of 0.34) and (ii) a smaller, presumably subclonal, population of cells characterized by additional alterations on chromosome 10, enrichment for proliferative subtype calls (P < 2.2 × 10⁻¹⁶, Fisher exact test), and increased subtype ambiguity (mean margin of 0.17). Tumor T90 displayed characteristics of a foldback inversion profile including a high degree of genomic rearrangements and a strong CCNE1 amplification (6, 7), and tumor T77 had a heterogeneous copy-number profile indicating presence of several subclonal cell populations. Subtype calls for the epithelial cells of both tumors (T77 and T90) were highly ambiguous between differentiated and proliferative (mean margin of 0.16 and 0.22, respectively).

For the three tumors with a larger number of epithelial cells captured and a substantial proportion of cells assigned to the proliferative subtype (T59, T77, and T90), we analyzed whether differences in subtype assignment can be explained by differences in cell-cycle activity. Therefore, we (i) inspected the expression levels of cell-cycle phase-specific cyclins and (ii) computationally assigned cells to different cell-cycle phases using the cyclone classifier (37). However, proliferative subtype calls could not be attributed to increased cell-cycle activity as cyclin expression was largely similar between differentiated and proliferative cells (Fig. 5B; Supplementary Fig. S16A–S16C). Exceptions were the upregulation of cyclin D1 in proliferative cells of tumor T59 and of cyclin E1 in proliferative cells of tumor T90, previously found associated with high proliferation rates in ovarian cancer cell lines (38) and estrogen receptor–positive breast cancer (39). Systematic assignment of cells to G₁-phase, G₂–M-phase, and S-phase using the cyclone classifier (37) displayed a distortion of cell-cycle scores toward the G₁-phase (Fig. 5C), a known hallmark of cancer (40), but proportions of cells assigned to different cell-cycle phases were not significantly different between subtypes (P = 0.15; proportion test, df = 2; Supplementary Table S2). As differences in subtype assignment thus did not seem to primarily result from differences in cell-cycle activity, we analyzed whether the expression of known HGSOC bulk tumor markers (3, 9) drives classification of epithelial cells as differentiated or proliferative. When sorting cells by subtype assignment and classification margin, we observed cross-tumor patterns of gradual downregulation of tumor differentiation markers and gradual upregulation of proliferation markers (Fig. 5D). This included downregulation of fallopian tube marker, SLPI (3), differentiation marker, LCN2 (41), and iron storage gene, FTH1 whose downregulation has been linked with more aggressive tumor characteristics and poor prognosis (42). This was paralleled by upregulation of markers of increased proliferative activity such as MARCKSL1 (43), MFAP2 (44), and transcription factor SOX4 whose upregulation is associated with the loss of epithelial features and late-stage high-grade malignant phenotypes (45), together indicating a transition from differentiated DIF-EPI cells to dedifferentiated/proliferative PRO-EPI cells.

We analyzed intratumor heterogeneity of HGSOC subtypes and investigated whether ambiguity in subtype classification can be attributed to the polyclonal composition of HGSOC tumors. This analysis addresses unresolved questions of whether proposed subtypes are early carcinogenic events that are reflected in later genomic development, whether they occur late in tumor development giving rise to subclonal expansion, or whether the notion of discrete HGSOC subtypes should be dismissed altogether. This distinction is clinically relevant because subtype-like changes occurring later in tumor development are likely to be heterogeneous in polyclonal tumors and are, thus, difficult targets for subtype-specific therapy, and furthermore, misplaced use of subtype terminology may interfere with a fuller understanding of HGSOC carcinogenesis. To infer the evolutionary timing of the development of subtype-like properties, we (i) analyzed recurrent SCNAs in TCGA tumors for subtype association and (ii) tested whether subtype-associated SCNAs tend to predominantly occur clonally (early) or subclonally (late). We first discuss genomic characteristics of the proposed subtypes based on SCNA subtype association and subclonality, and contrast these findings with the results from the single-cell analysis of six HGSOC tumors. We then propose a more realistic model of continuous tumor development that includes mixtures of subclones and evolution between properties previously associated with discrete subtypes, and discuss possible implications for chemotherapy.

Reliable inference of subtype evolution from bulk tumor genomics is possible because recurrent CN alterations in HGSOC are associated with the proposed transcriptome subtypes. Absolute CN analysis from SNP arrays and whole-exome sequencing allows detection of subclonal alterations (27, 32), and thus, inference of the subclonality of proposed subtypes. As reported elsewhere (32), we also found individual CN calls and per-tumor estimates of purity and ploidy to be consistent across experimental platforms (SNP arrays and whole-exome sequencing) and computational methods (ABSOLUTE; ref. 27 and PureCN; ref. 33). To demonstrate this novel approach, we showed that STS subtypes are driven by clonal SCNAs that occur early in tumor development. HGSOCs and STSs are both characterized by recurrent SCNAs and low levels of somatic mutations (34); comparable analysis of other cancer types that are mainly driven by somatic mutations would require incorporating allele-specific analysis of somatic mutations (27, 33).

In HGSOC, proliferative tumors are associated with a large number of amplifications, higher ploidy, and increased frequency of genome duplication. Amplifications associated with proliferative tumors drive a pattern that subtype-associated SCNAs tend to be more frequently subclonal than other alterations. In contrast, differentiated tumors display lower alteration frequency, close-to-normal ploidy, and smaller fractions of subclonal alterations. Thus, differentiated and proliferative tumors appear to represent different ends of an evolutionary time scale. Because tumors generally evolve from a founder clone with fewer genomic lesions to multiple clones with accumulated lesions (46), these observations indicate that the proposed subtypes are more likely stages along such a process of tumor evolution.

Single-cell analysis further suggests transitions from the differentiated toward the proliferative spectrum. Tumor epithelial cells with fewer copy-number alterations are more confidently classified as differentiated, expressing signals associated with fallopian tube tissue identity and differentiation of the founder clone. Higher levels of genomic rearrangements, typically detectable in subclones, are more frequently found in epithelial cells classified as proliferative, expressing a heterogeneous set of markers associated with more aggressive tumor characteristics, loss of epithelial cell features, and poor prognosis. Single-cell analysis further confirms large differences in proportion of epithelial, immune, and stromal cells between tumors. Assignments to the immunoreactive and mesenchymal spectrum were driven by large fractions of tumor-infiltrating immune and stromal cells, respectively, consistent with lower purity and with reports linking the immunoreactive and mesenchymal subtypes to the tumor microenvironment (14, 31, 47). The observation that discrete subtype calls were frequently less confident for single cells than for the bulk tumor further supports the notion that the proposed bulk subtypes are not well defined. The observed ambiguity in classification of single cells did not result from the low coverage of scRNA-seq data (35), as downsampling of bulk RNA-seq data to match the coverage of the scRNA-seq data still allowed for a more confident subtype assignment. Instead, the bulk tumor classifier can be understood as marking (i) cell type proportions and (ii) accumulation of mutations in cancer epithelial cells, with only the latter being relevant to classification of single epithelial cells. Ambiguity in classification of single epithelial cells reflects that the bulk tumor classifier contains many signature genes not expressed in individual epithelial cells or expressed along a continuous spectrum, as opposed to signature genes not being sufficiently expressed for detection at single-cell level. This is consistent with observations (9, 14) that each HGS ovarian tumor is represented by multiple expression signatures at different levels of activation, which are effectively deconvolved at single-cell level. Therefore, these observations also question the utility of the proposed transcriptome subtypes for describing distinct molecular features of individual HGS ovarian cancer cells. As molecular subtypes at bulk resolution are largely determined by tumor composition with the most abundant cell type driving classification, their application does not seem to be meaningful at single-cell resolution. Although the proposed subtypes might still provide for a convenient summary of a tumor with predictive value, our findings prompt development of a separate subtyping scheme for individual HGS ovarian cancer cells as a foundation for establishing whether true intrinsic HGSOC subtypes, as characterized by different molecular features, for instance, described for STS, exist.

Rather than supporting the existence of discrete subtypes, our observations are more consistent with a model that places the differentiated and the proliferative subtype at opposite ends of the timeline of tumor development, with characteristics of differentiated tumors occurring earlier in malignancy, and characteristics of proliferative tumors occurring at a later time. Increasing genomic instability and subclonal expansion along this timeline, likely spanning several years, is consistent with previous reports (21, 48). Furthermore, (i) mean patient age at diagnosis was lowest for differentiated tumors (55 years), and highest for proliferative tumors (64 years; ref. 11); and (ii) differentiated tumors displayed the highest levels of lymphocyte infiltration (>40%; ref. 11), indicating an active immune response at an earlier stage of tumor development. In contrast, proliferative tumors displayed negligible lymphocyte infiltration (<5%; ref. 11), consistent with an adapted tumor successfully evading the immune response at a later point in evolution (49). Immunoreactive tumors are associated with infiltrating macrophages and different histopathologic classification (50); and mesenchymal tumors with stromal expression (14, 47) and potentially with different tissue of origin (12). Although immunoreactive and mesenchymal tumors may thus represent different evolutionary starting points, characteristics of both might also result from different cellular mixtures occurring along the same evolutionary timeline (Supplementary Discussion S3.1; Supplementary Fig. S17A–S17C).

We therefore propose dismissing the model of discrete subtypes for HGSOC. While tumors of different patients display distinctive properties, we propose understanding these properties as existing on a spectrum, and as having the potential to change over the course of tumor development. This interpretation is consistent with the reported continuum of HGSOC genomes shaped by individual CN signatures (Supplementary Discussion S3.2; Supplementary Fig. S18; ref. 7), and consolidates and extends previous reports on extensive temporal and spatial heterogeneity in HGSOC (21, 48). The presence of different sources of heterogeneity presents a challenge for effective therapy, because tumor evolution over the course of therapy and relapse has been repeatedly attributed to drug-resistant subclones, which expand under the selective pressure of therapeutic intervention (Supplementary Discussion S3.3; Supplementary Fig. S19A and S19B; refs. 21, 48). Therapies targeting genomic mutations should thus focus on events occurring earlier in tumor evolution, because those occurring later will be subclonal, even if they grow to dominate the tumor. For HGSOC, such early clonal alterations tend to be deletions, including frequent loss of RB1, NF1, and PTEN (Supplementary Figs. S4 and S6), which are, however, difficult therapeutic targets (51).

In this study, we investigated whether ambiguity of gene expression–based HGSOC subtypes results from intrinsic ambiguity at the level of individual tumor cells or from a mixture of subclones of different defined subtypes. Although subtype-associated DNA alterations tend to occur more frequently subclonal, we found this to be merely attributable to the overall higher ploidy and subclonality of tumors of the proliferative spectrum, rather than providing evidence for the existence of subclones with different well-defined subtypes. On the other hand, subtype classification on single cells demonstrated that ambiguity results from a combination of (i) mixture of cell types at bulk level, and (ii) apparently evolving characteristics from one subtype spectrum to another. From these observations, we conclude that the notion of discrete subtypes does not realistically represent the heterogeneity and genomic complexity of HGSOC. Instead, we propose that HGSOC is characterized by heterogeneity defined primarily by tumor evolution and composition. This perspective is in agreement with recent genomic classifications of HGSOC (6, 7), which reported temporal and spatial heterogeneity (21, 48), and reconciles findings from bulk tumor and single-cell analysis. In this model, tumors evolve from a largely intact genome (early-differentiated spectrum) toward a comprehensive loss of genome integrity (late-proliferative spectrum), driven by stochastic and individually different genomic alterations from a constrained set of evolutionary moves that give rise to increasing genomic instability and subclonal expansion. Together with heterogeneity in tumor purity and composition, driving assignment of tumors to the immunoreactive and the mesenchymal spectrum, this provides an explanation for ambiguity in subtype classification that exists also on the cellular level to an extent even exceeding classification ambiguity of the bulk tumor. Experimental validation of this model using human samples will be challenging, as it is not feasible to collect longitudinal samples from a patient as their tumor evolves; longitudinal samples could be obtained from transgenic mice that develop autochthonous ovarian cancer. Molecular analysis of early-stage HGS ovarian tumors from multiple independent cohorts provides supporting evidence as the majority of these tumors have a differentiated phenotype with low numbers of SCNAs (Supplementary Table S3). As a strong relationship exists between patient age and tumor stage of HGSOC (3, 52), this implies that an earlier stage is likely at least partly due to earlier detection, as opposed to a more indolent tumor, and that stages likely develop over decades. With the availability of more single-cell data for HGSOC in the future, it will be possible to more comprehensively study tumors at critical transition stages before, during, and after treatment.

B.J. Winterhoff reports grants from Ovarian Cancer Research Alliance (Liz Tilberis Early Career Award) and University of Minnesota (Grand Challenges Research Program) during the conduct of the study. S.A. Mullany reports grants from OCRA during the conduct of the study. M. Morgan reports grants from US NIH during the conduct of the study. G. Parmigiani reports a patent for WO2014153442A2 Methods and systems for treatment of ovarian cancer issued. L.-X. Qin reports grants from NIH (R21 CA214845) during the conduct of the study. M. Riester is a full-time employee of Novartis outside the submitted work. T.K. Starr reports grants from Ovarian Cancer Research Alliance (The Ovarian Cancer Research Alliance provided funding for experiments reported in this article) during the conduct of the study. No potential conflicts of interest were disclosed by the other authors.

L. Geistlinger: Conceptualization, resources, data curation, software, formal analysis, visualization, methodology, writing-original draft, writing-review and editing. S. Oh: Software, formal analysis, visualization, methodology. M. Ramos: Resources, data curation, software. L. Schiffer: Resources, data curation, software. R.S. LaRue: Resources, data curation, software, formal analysis. C.M. Henzler: Resources, data curation, software, formal analysis. S.A. Munro: Resources, data curation, formal analysis. C. Daughters: Resources, data curation. A.C. Nelson: Resources, data curation, supervision, funding acquisition, project administration. B.J. Winterhoff: Resources, data curation, formal analysis. Z. Chang: Resources, data curation. S. Talukdar: Resources, data curation. M. Shetty: Resources, data curation, project administration. S.A. Mullany: Resources, data curation. M. Morgan: Software, funding acquisition, project administration. G. Parmigiani: Conceptualization, formal analysis, methodology, writing-review and editing. M.J. Birrer: Conceptualization, methodology, writing-review and editing. L.-X. Qin: Formal analysis, writing-review and editing. M. Riester: Conceptualization, resources, software, formal analysis, supervision, methodology, writing-review and editing. T.K. Starr: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, methodology, writing-original draft, project administration, writing-review and editing. L. Waldron: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, methodology, writing-original draft, project administration, writing-review and editing.

L. Geistlinger was supported by a research fellowship from the German Research Foundation (GE3023/1-1 and GE3023/1-2). R.S. LaRue, C.M. Henzler, A.C. Nelson, B.J. Winterhoff, and T.K. Starr were supported by grants from the Jan Chorzempa Cancer Research Endowed Fund (to T.K. Starr), the Masonic Cancer Center's Translational Working Group Grant (to T.K. Starr), University of MN Grand Challenges grant (to B.J. Winterhoff, A.C. Nelson, and T.K. Starr), the American Cancer Society CSDG (#132574-CSDG-18-139-01-CSM to A.C. Nelson), and institutional grants to the University of Minnesota from NIH/NCI P30CA07759821 and CTSI NCATS UL1TR00249402. G. Parmigiani was supported by grant 5P30CA006516-53, and L. Waldron by grants U24CA180996 and 1R03CA191447-01A1 from the NCI of the NIH.

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