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Bulk transcriptomic analyses of high-grade serous ovarian cancer (HGSOC) so far have not uncovered potential drug targets, possibly because subtle, disease-relevant transcriptional patterns are overshadowed by dominant, non-relevant ones. Our aim was to uncover disease-outcome-related patterns in HGSOC transcriptomes that may reveal novel drug targets. Using consensus-independent component analysis, we dissected 678 HGSOC transcriptomes of systemic therapy naïve patients—sourced from public repositories—into statistically independent transcriptional components (TCs). To enhance c-ICA’s robustness, we added 447 transcriptomes from non-serous histotypes, low-grade serous, and non-cancerous ovarian tissues. Cox regression and survival tree analysis were performed to determine the association between TC activity and overall survival (OS). Finally, we determined the activity of the OS-associated TCs in 11 publicly available spatially resolved ovarian cancer transcriptomes. We identified 374 TCs, capturing prominent and subtle transcriptional patterns linked to specific biological processes. Six TCs, age, and tumor stage stratified patients with HGSOC receiving platinum-based chemotherapy into ten distinct OS groups. Three TCs were linked to copy-number alterations affecting expression levels of genes involved in replication, apoptosis, proliferation, immune activity, and replication stress. Notably, the TC identifying patients with the shortest OS captured a novel transcriptional pattern linked to synaptic signaling, which was active in tumor regions within all spatially resolved transcriptomes. The association between a synaptic signaling-related TC and OS supports the emerging role of neurons and their axons as cancer hallmark-inducing constituents of the tumor microenvironment. These constituents might offer a novel drug target for patients with HGSOC.
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Introduction
Epithelial ovarian cancer encompasses five primary histological subtypes, with HGSOC constituting about 75% of all cases (Lheureux et al., 2019). The standard treatment for HGSOC diagnosed at stage IIB and beyond involves a combination of surgery and chemotherapy, primarily using platinum-based compounds and taxanes (NCCN Guidelines, 2019; Wright et al., 2016). While initial chemotherapy results in tumor response in most patients with HGSOC, there is a very high recurrence rate (Corrado et al., 2017). The addition of poly-ADP ribose polymerase and vascular endothelial growth factor A inhibitors to chemotherapy for subsets of patients currently results in a 5 y disease-specific overall survival (OS) rate of approximately 45% for patients with HGSOC. This rate has hardly improved in the last three decades (Ledermann, 2016; Wang et al., 2018; Wu et al., 2019; Tewari et al., 2019). Therefore, new insights into the complex biology underlying HGSOC are urgently needed to develop more effective treatment strategies.
Previous studies using bulk transcriptomes of patients with HGSOC have identified expression-based molecular subtypes. However, these subtypes did not provide insights that have translated into novel drug targets (Bell et al., 2011; Tothill et al., 2008; Verhaak et al., 2013). A common limitation of such studies is their reliance on bulk transcriptomes, containing both tumor cells and tumor microenvironment (TME) components, thus reflecting the average transcriptional patterns of the combination of all biological processes present in the tumors. This averaging often masks subtle transcriptional patterns pivotal to understanding HGSOC biology, especially when these are overshadowed by dominant patterns from other less relevant (non-)biological processes (Chen et al., 2011). Consensus-independent component analysis (c-ICA) offers an alternative by decomposing such bulk transcriptomes into statistically independent transcriptional patterns (i.e. transcriptional components; TCs) (Kong et al., 2008; Chiappetta et al., 2004). This approach reveals both dominant and subtle patterns and provides a measure of TC activity for each sample (Biton et al., 2014).
In the present study, our aim was to utilize c-ICA to dissect HGSOC transcriptomes to identify as many TCs associated with patient OS as possible, which could reveal potential novel drug targets.
Methods
See the appendix for the extended methods.
Data acquisition
Raw microarray bulk transcriptomes and clinicopathological details for patients with HGSOC, low-grade serous ovarian cancer (LGSOC), non-serous ovarian cancer, and benign ovarian tissues were sourced from the Gene Expression Omnibus (GEO)(Clough and Barrett, 2016). We exclusively utilized transcriptomes generated from primary tumor samples. Our analysis was confined to samples on the Affymetrix HG-U133 Plus 2.0 platform (GEO accession identifier: GPL570) and excluded cell line samples. The datasets were pre-processed and quality controlled as previously described (Fehrmann et al., 2015). Furthermore, for comprehensive analyses, we incorporated transcriptomes from five distinct resources: the Cancer Cell Line Encyclopedia (CCLE, n=969), Genomics of Drug Sensitivity in Cancer (GDSC, n=959), Gene Expression Omnibus (GEO, n=13,810), and The Cancer Genome Atlas (TCGA, n=8150), and spatially resolved transcriptomes from 10xGenomics (Clough and Barrett, 2016; Barretina et al., 2012; Yang et al., 2013; Barrett et al., 2013).
Consensus-independent component analysis (c-ICA)
To preprocess the bulk transcriptome data, we applied a whitening transformation to prepare it for subsequent analysis. Consensus-ICA was conducted as described previously (Knapen et al., 2024). The output of a c-ICA includes two matrices: (i) transcriptional components (TCs) with gene weights, where each weight within the TC represents both the direction and magnitude of its effect on the expression levels of each gene, and (ii) a consensus mixing matrix (MM) with its coefficients representing the activity scores of TCs across samples.
Survival analysis
To discern the relationship between TC activity and patient OS, a univariate Cox proportional hazards analysis was conducted on a select group of patients with available follow-up data (n=541, Supplementary file 1). In addition, a multivariate Cox proportional hazards analysis was carried out, including covariates such as age, stage, debulking status, and tumor grade. This latter analysis was based on a subset of patients with comprehensive clinicopathological data available (n=373, Supplementary file 1). We implemented a multivariate permutation framework encompassing 10,000 permutations to mitigate the risk of false discoveries. We established the acceptable false discovery rate (FDR) at 1%, maintaining an 80% confidence level, applicable for both the univariate and multivariate analyses.
Survival tree analysis
We performed a survival tree analysis to delineate groups of patients with HGSOC treated with platinum-based chemotherapy based on distinct transcriptional and clinicopathological attributes. The analysis utilized activities of TCs associated with OS (either from univariate or multivariate survival analysis as mentioned in supplementary methods) in conjunction with relevant clinicopathological factors, such as age, tumor stage, debulking status, and grade, as potential classifiers. We divided patients into two subsets using every plausible cut-off point for each classifier and compared the resulting survival curves employing the log-rank statistic. Consequently, the division was based on the most significant classifier at its optimal cut-off based on the smallest p-value of the log-rank test mentioned above. This divisional process was successfully reiterated on the derived subsets until any of the following stipulated conditions was satisfied: (i) the total patient count across both subsets fell below 50, (ii) the collective number of uncensored events in both subsets was <25, or (iii) one of the subsets contained <17 patients. To gauge the stability of our classifiers, we performed 20,000 iterations, randomly selecting 80% of the patient group in each iteration. The significance-based ranks of classifiers in these iterations were correlated with those from the primary survival tree.
Associating the identified transcriptional components with biological processes
To discern the biological processes associated with the TCs, we adopted a multifaceted approach encompassing (i) Transcriptional Adaptation to Copy Number Alterations (TACNA) profiling, targeting the identification of TCs that reflect the downstream implications of copy number alterations (CNAs) on gene expression levels (Bhattacharya et al., 2020); (ii) Execution of gene set enrichment analysis (GSEA) for each TC, utilizing gene set collections (n=16) from The Human Phenotype Ontology (The Monarch Initiative), the Mammalian Phenotypes (Mouse Genome Database), and the Molecular Signatures Database (MsigDB) Subramanian et al., 2005; Köhler et al., 2019; (iii) The formation of co-functionality networks on the top and bottom genes of each TC, achieved using the GenetICA methodology, accessible via https://www.genetica-network.com (Urzúa-Traslaviña et al., 2021). For clusters comprising ≥5 genes, the enrichment of the predicted functionality was quantified. This served as the foundation for determining the biological process associated with the TC being examined.
Cross-study transcriptional component projection
To determine whether a biological process captured by an identified TC is also active in other cancer types and to investigate if it is more active in tumor cells or in the TME, we collected raw expression profiles from multiple sources: the CCLE, n=969, GDSC, n=959, GEO, n=13,810, and TCGA, n=8150 (Clough and Barrett, 2016; Barretina et al., 2012; Yang et al., 2013; Barrett et al., 2013). While the CCLE and GDSC datasets comprise cell line profiles across many solid and hematologic malignancies, the GEO and TCGA datasets offer an extensive set of bulk transcriptomes derived from patient samples spanning 27 tumor types. We pre-processed the raw data as previously described (Bhattacharya et al., 2020). Next, we projected the TCs identified via c-ICA onto the cell line expression profiles from CCLE and GDSC and the patient-derived expression profiles from GEO and TCGA. This projection methodology has been described in more detail previously (Bhattacharya et al., 2020). To identify potential variations in the activity scores of the TCs, we compared the activity scores among cell lines and samples derived from patients within all four repositories. We used an absolute activity score threshold of 0.05 for each TC to pinpoint outlier cell lines and patient tumors with heightened activity.
Determination of spatial transcriptomic profiles’ significant activity locations for individual transcriptional components
To further assess whether a biological process captured by an identified TC is more active in tumor cells or in the TME, we collected publicly available spatial resolved transcriptomic profiles of ovarian cancer samples. Eight were sourced from GEO (study ID GSE211956), and three were sourced from the public dataset repository of 10xGenomics (see supplementary methods for details) generated using the 10xGenomics Visium platform. The samples were from patients with HGSOC, serous papillary, and endometrioid ovarian cancer. Activity for each TC across every location within the spatial samples was ascertained through the cross-study projection methodology referred to in the previous method section (Bhattacharya et al., 2020). We incorporated a permutation-driven approach to discern the markedly active areas within the spatial samples for each TC. We derived a null distribution of activities for each TC-location pairing by performing 3000 permutations and subsequent projections. The p-value of each observed TC activity quantifies the significance of the deviation of the TC activity at a given location from its baseline null distribution. After this, we visualized the z-transformed p-values using a heatmap, followed by obtaining colocalization scores for each combination of TCs in the spatial transcriptomic profiles for each ovarian cancer sample (Grisanti Canozo et al., 2022). This visualization aided in highlighting the areas with notable activity aligned against the stained representation of the tissue sample.
Results
An integrated data set containing 1125 bulk transcriptomes from ovarian tissues
We curated 1193 bulk transcriptomes from the GEO, including patients with HGSOC, LGSOC, non-serous ovarian cancer, and benign ovarian tissues (Clough and Barrett, 2016). These were extracted from 32 distinct studies and represented the entire spectrum of ovarian cancer types, stages, and grades, and included 43 samples from non-malignant ovarian tissue. Pre-processing, which included removing duplicates and quality checks, culminated in a refined dataset of 1125 samples (Fehrmann et al., 2015). Supplementary file 1; Supplementary file 2 provide detailed breakdowns of these samples, showcasing the comprehensive coverage of ovarian cancer types, stages, and grades within this dataset. The ovarian cancer dataset comprised bulk transcriptomes of patients with HGSOC (n=678), other serous (n=110), endometrioid (n=110), and clear-cell ovarian cancer samples (n=96). Additionally, for 541 patients, comprehensive survival data was available, as well as additional clinicopathologic information, including age, grade, stage, subtype, treatment schedule, and debulking status for 373 patients (Figure 1).
Figure 1.
Workflow indicates the data acquisition and relations between the methods.
Consensus-independent component analysis identifies 374 transcriptional components (TCs)
c-ICA on the 1125 bulk transcriptomes revealed 374 independent TCs. Notably, 135 TCs captured the impact of copy number alterations on gene expression levels. Each TC displayed enrichment for at least one gene set from the 16 gene set collections, with an absolute Z-score of more than two. For example, the number of enriched gene sets from the Hallmark gene set collection in an individual TC ranged from zero to 28 enriched gene sets (interquartile range 3–7). The median top Z score for Hallmark gene sets was 3.21 (range 1.55–37.54, interquartile range 2.6–4.25). A comprehensive database, including all TCs and GSEA outcomes, has been made accessible at http://transcriptional-landscape-ovarian.opendatainscience.net.
The activities of 13 TCs were associated with patient overall survival (OS) in a univariate analysis, with an additional TC (TC166) identified in a multivariate analysis accounting for age, stage, debulking, and tumor grade. Combined, these 14 OS-associated TCs were enriched for gene sets associated with diverse biological processes and clinicopathological characteristics, with four TCs capturing the effects of copy number alterations on gene expression levels.
The activities of six transcriptional components are associated with patient overall survival
For a selected subset of 541 patients—including HGSOC, LGSOC, and non-serous ovarian cancer—with available OS information (Supplementary file 1), 13 TC activities displayed an association with OS univariately (false discovery rate of 5%, confidence level of 80% in permutation-based multiple testing framework Supplementary file 3; Figure 2). For patients with serous ovarian cancer, treated with platinum-based therapy (n=301, Supplementary file 1), lower activity of one additional TC (TC166) was associated with worse OS independent of age, stage, debulking, and tumor grade (Supplementary file 4). Combined, these 14 OS-associated TCs were enriched for gene sets associated with diverse biological processes and clinicopathological characteristics. Four of these TCs captured the downstream effects of CNAs on gene expression levels (Figure 2, Figure 2—figure supplements 1–3). Survival tree analysis identified ten groups of patients with platinum-treated HGSOC based on the activity of six OS-associated TCs and the presence of two clinicopathological characteristics, namely age and stage (Figure 3, Supplementary file 5, median robustness statistic of survival tree = 0.52, interquartile range = 0.36–0.69). The survival tree demonstrated good classification power (concordance statistic = 0.72, standard error = 0.021). As expected, patients were divided into separate survival groups based on stage (1/2 vs 3/4) and age (<53.7 vs ≥53.7 y). The most significant difference in OS was observed between the cohorts with low and high TC121 activity (Supplementary file 5). Patients with high TC121 tumor activity exhibited the shortest OS, also observed for the subset of patients with advanced-stage HGSOC (Figure 3—figure supplement 1, Supplementary file 6). Figure 3—figure supplement 2 indicates that TC121 activity is highest in patients with HGSOC compared to other ovarian cancer subtypes. Notably, all subtypes contain subsets of samples with elevated TC121 activity. These robust associations with OS for TC121 in these two subsets of patients indicate the relevance of TC121, irrespective of stage.
Figure 2.
Enrichment heatmap of hallmark gene sets in transcriptional components associated with patient overall survival.
Gene Set Enrichment Analysis for 14 transcriptional components (TCs) associated with overall survival (OS) identified through univariate or multivariate survival analyses are presented. Only Hallmark gene sets with significant enrichment (Bonferroni-corrected p-value) for at least one TC are shown. The heatmap displays Z-scores, which indicate the relative enrichment strength, with values truncated at a maximum of 4 for visualization purposes. The gene sets were clustered based on Pearson correlation using the Ward D2 method, providing insights into related biological processes captured by different TCs. In the right column, chromosomal locations of copy number alterations (CNAs) are shown, reflecting the downstream effects on gene expression that each TC captures. This integration of CNA information highlights the biological relevance of each TC and its contribution to gene expression variability and patient outcomes.
Figure 2—figure supplement 1.
Association of OS-related TCs with clinicopathologic parameters.
The association between clinicopathologic parameters and the activity of the OS-associated 14 TCs was determined in the complete set of 1125 samples. Pearson correlation was used to calculate the association of each clinicopathologic parameter with each TC. The TCs were then ranked based on their association with OS. Abbreviations: TC = transcriptional component, OS = overall survival.
Figure 2—figure supplement 2.
Enrichment heatmap for the KEGG gene set collection in OS-related TCs.
Gene set enrichment analysis (GSEA) results of 14 TCs associated with OS in univariate or multivariate survival analysis are presented, including KEGG gene sets that were included in the enrichment for at least one TC that passed the Bonferroni threshold for multiple testing correction. The gene sets were clustered using Pearson correlation and Ward D2, and the heatmap colors were based on Z-scores, truncated at a value of four. The right column shows the chromosomal location of a copy number alteration that the TC captures the downstream effects on gene expression levels. Abbreviations: TC = transcriptional component, OS = overall survival.
Figure 2—figure supplement 3.
Enrichment heatmap for the REACTOME gene set collection in overall survival (OS)-related TCs.
Gene set enrichment analysis (GSEA) results of 14 TCs associated with OS in univariate or multivariate survival analysis are presented, including REACTOME gene sets that were included if the enrichment for at least one TC passed the Bonferroni threshold for multiple testing correction. The gene sets were clustered using Pearson correlation and Ward D2, and the heatmap colors were based on Z-scores, truncated at a value of four. The right column shows the chromosomal location of a copy number alteration that the TC captures the downstream effects on gene expression levels. Abbreviations: TC = transcriptional component.
Figure 3.
Survival tree analysis of patients with platinum-treated HGSOC defines survival cohorts with distinct clinicopathologic and biological characteristics.
The results of the survival tree analysis of 294 patients with high-grade serous ovarian cancer (HGSOC) treated with platinum-based chemotherapy are presented. The analysis utilized 14 transcriptional components (TCs) associated with overall survival (OS), along with other clinicopathologic factors, including age, tumor stage, grade, and debulking status. The resulting tree identified nine distinct survival cohorts, each represented as a bar in the Sankey diagram, where the bar height corresponds to the number of patients in each cohort. Kaplan-Meier survival curves with accompanying number-at-risk tables are shown for each cohort, with survival data censored at 10 y. The names of the survival cohorts were based on enriched biological processes in the TCs, as determined by the chromosomal location of genes captured by a TC, GSEA, and co-functionality analysis of the top genes. The p-values in the Kaplan-Meier plots were derived from log-rank tests comparing survival distributions between groups. Abbreviations: TC = transcriptional component, ECM = extracellular matrix.
Figure 3—figure supplement 1.
Survival tree analysis of patients with advanced-stage, high-grade serous ovarian cancer (HGSOC) defines survival cohorts with distinct clinicopathologic and biological characteristics.
Survival tree analysis of 265 patients with advanced-stage, platinum-treated HGSOC using 14 OS-associated TCs and other classifiers such as age, tumor stage, grade, and debulking status. The analysis resulted in nine survival cohorts, and the height of the bar in the Sankey diagram represents the number of patients in each cohort. The Kaplan-Meier plots and number-at-risk tables are presented with survival data censored at 10 y. The names of the survival cohorts were based on enriched biological processes in the TCs, as determined by the chromosomal location of genes captured by a TC, gene set enrichment analysis (GSEA), and co-functionality analysis of the top genes. The p-values in each panel show the p-value from the corresponding log-rank test between the two groups. Abbreviations: TC = transcriptional component, ECM = extracellular matrix.
Figure 3—figure supplement 2.
The activity of TC121 in bulk transcriptomes of patients with different subtypes of ovarian cancer.
The boxplots display the activity scores of TC121 in different cancer subtypes, which are ordered based on their corresponding median activity scores.
Figure 3—figure supplement 3.
Three overall survival (OS)-associated TCs capture the transcriptional effect of copy number alterations.
For each OS-related TC, the weight of each gene was plotted on its genomic location. Abbreviations: TC = transcriptional component.
Distinct biological processes show enrichment in the transcriptional components associated with overall survival
Three of the six TCs associated with OS—TC166, TC247, and TC76—captured the effects of CNAs on the expression levels of genes mapping to chromosome regions 13q12-q14, 11q13-q14, and 9p13-p21, respectively (Figure 3—figure supplement 3, Supplementary file 7; Wang et al., 2018). The higher activity of TC166 was associated with better OS, whereas the higher activities of TC121, TC247, TC250, TC76, and TC146, were associated with worse OS. Among the 14 OS-associated TCs, only TC166 showed a significant association with OS in an independent cohort of patients with ovarian clear cell carcinoma (Bonferroni corrected p-value <0.05; see supplementary methods and Supplementary file 8: Bolton et al., 2022). The top genes from TC166 were enriched for genes involved in replication and apoptosis. The chromosomal region 13q12-q14 linked to the TC166 contains the tumor suppressor genes retinoblastoma 1 (RB1) and Breast Cancer Type 2 Susceptibility Protein (BRCA2). Loss of heterozygosity of this chromosomal region is frequently observed in both sporadic and hereditary serous ovarian cancers (Huang et al., 2012; Jongsma et al., 2002). The top genes from TC247 were enriched for genes involved in proliferation and immune cell activation, TC76 in replication stress, TC250 in extracellular matrix (ECM) interactions, and TC146 in neurotransmitter signaling.
Intriguingly, the top 100 genes in TC121 revealed a co-functional cluster enriched for genes involved in synaptic signaling, with the corresponding proteins reported to localize to the synaptic membrane of neurons (Figure 4). Among these were pre-synaptic protein neurexin-1 (NRXN1) and its post-synaptic ligand leucine-rich repeat transmembrane protein 2 (LRRTM2), which regulates excitatory synapse formation (top 20 genes are described in Supplementary file 9, for more details: http://transcriptional-landscape-ovarian.opendatainscience.net) (Ko et al., 2009; de Wit et al., 2009). Furthermore, this co-functional cluster included neuron-specific synaptic structure proteins, neurofilament light, and medium chain. Moreover, genes encoding for potassium ion channel proteins integral to membrane repolarization during synapse signal transduction carried high weights in TC121. These genes included KCNC1, KCNN2, and KCNIP1 (Bourdeau et al., 2011; Ried et al., 1993; Willis et al., 2017). Several genes in TC121 encoded proteins related to glutamate receptor signaling, including GRIN2C and SLC7A10 (Ehmsen et al., 2016). In line with this proposed function, high activity of TC121 was observed in neuroblastoma cell lines but not in ovarian or central nervous system cancer cell lines in the GDSC and CCLE datasets (Figure 5A, Figure 5—figure supplements 1 and 2). In the GEO and TCGA datasets, high activity of TC121 was observed in glioblastoma multiforme and lower-grade glioma but not in ovarian cancer patient samples (Figure 5B).
Figure 4.
Co-functionality network of top 100 absolute weighted genes in TC121.
Co-functionality network for the top 100 genes with the highest absolute weights in TC121 is presented. Genes were clustered based on predicted co-functionality (r>0.7) across datasets, with clusters identified using both Gene Ontology (GO) Biological Processes and Cellular Components databases. One primary cluster, containing more than five genes, exhibited strong enrichment for synaptic signaling in the GO Biological Processes database and for synaptic membranes in the GO Cellular Components database. This highlights the biological specificity of TC121 in regulating gene expression linked to synaptic functions.
Figure 5.
The activity of TC121 in bulk transcriptomes of Cancer Cell Line Encyclopedia (CCLE), Genomics of Drug Sensitivity in Cancer (GDSC) cell lines, and Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) patient-derived samples.
(A) Cross-study TC projection of TC121 on CCLE and GDSC cell lines. The boxplots display the activity scores of TC121 in different tissue types, which are ordered based on their corresponding median activity scores. (B) Cross-study TC projection of TC121 on GEO and TCGA bulk transcriptomes resulted in the activity scores presented in the boxplots. Cancer types were ordered based on corresponding medians of TC121 activity scores. Abbreviations: TC = transcriptional component.
Figure 5—figure supplement 1.
Activity of overall survival (OS)-associated TCs in bulk transcriptomes of Cancer Cell Line Encyclopedia (CCLE) cell lines.
The proportion of samples with outlier activity scores in individual tissue types for each of the 14 OS-associated TCs was obtained using an absolute cut-off value of 0.05. TCs were ordered based on their association with OS. Abbreviations: TC = transcriptional component.
Figure 5—figure supplement 2.
The activity of overall survival (OS)-associated TCs in bulk transcriptomes of Genomics of Drug Sensitivity in Cancer (GDSC) cell lines.
The proportion of samples with outlier activity scores in individual tissue types for each of the 14 OS-associated TCs was obtained using an absolute cut-off value of 0.05. TCs were ordered based on their association with OS. Abbreviations: TC = transcriptional component.
Figure 5—figure supplement 3.
Association heatmap for The Cancer Genome Atlas (TCGA) cluster sets with the activity scores of OS-related TCs.
This heatmap highlights the associations between each cluster set and the TCs, represented by log-transformed p-values from the corresponding Kruskal-Wallis test. A significant association between a TC and a cluster set indicates that at least one cluster within the cluster set exhibited significantly different activity scores for the corresponding TC compared to the other clusters. The heatmap colors were based on log-transformed p-values. Abbreviations: TC = transcriptional component. OS = overall survival.
Distinct cluster of patients from TCGA overlaps with elevated activity of TC121
To explore if pre-existing classifications of patients with ovarian cancer correspond to the contrasting activities of the TCs, we investigated the classification provided by TCGA. TCGA identified four optimal clusters describing the patients with ovarian cancer using transcriptional profiles (Bell et al., 2011). To explore associations between these clusters and TC activity, we performed a Kruskal-Wallis test using TCGA sample data. Figure 5—figure supplement 3 highlights the associations between each cluster set and the TCs, represented by log-transformed p-values. A significant association between a TC and a cluster set indicates that at least one cluster within the cluster set exhibited significantly different activity scores for the corresponding TC compared to the other clusters. Notably, samples with high TC121 activity were not captured by any of the clusters of the four-cluster set. Interestingly, the eight-cluster set predefined by TCGA was able to identify a cluster that corresponded to samples with elevated TC121 and TC146 activity. This finding suggests that while TCGA’s analysis identified this patient group based on transcriptional profiles, it didn’t characterize them further.
Distinct spatial and single-cell transcriptional profiles with high activity of OS-associated TCs
Cross-study TC projection onto spatial transcriptomic profiles from 11 ovarian cancer samples revealed that TC121 was highly active in profiles from the tumor region of the 11 ovarian cancer samples (Figure 6A; Figure 6—figure supplement 1). Additionally, TC121 showed markedly higher activity in the transcriptional profiles of a subset of the unannotated single cells from HGSOC patients (study ID GSE158722; see supplementary methods and Figure 6—figure supplement 2).(Nath et al., 2021) This finding suggests that some of these unannotated cells could be neurons. Furthermore, the unannotated single-cell transcriptional profiles showed contrasting activity scores of different OS-associated TCs (Figure 6—figure supplement 2). These contrasting activities indicate that these TCs could provide insights into the biology of previously uncharacterized cell types. Distinct regions with high activity of the copy number TCs (TC166, TC247, and TC76) in the HGSOC sample overlapped with the region containing cancer cells, as expected. TC250, enriched for extracellular matrix interactions, was also active in the stromal region. The strongest inverse colocalization (colocalization score –2.43) was observed between the activity scores of TC146, enriched for neurotransmitter signaling, and TC76, which captured the effect of copy number alterations at chromosome 9p13-p21, at the serous ovarian cancer sample (Figure 6B, Supplementary file 10).
Figure 6.
Spatial transcriptomic profiles in ovarian cancer samples.
(A) We employed a permutation-based approach to pinpoint the areas of significant TC activity in spatial transcriptomic profiles. We ran 5000 permutations for each TC-profile combination, yielding a p-value that indicates the extent to which the TC activity in the corresponding profile differs from what would be expected by chance (the null distribution). We then transformed these p-values into logarithmic values and represented them using a heatmap. Heatmaps of activity scores of the TCs are presented in individual rows for the high-grade serous ovarian cancer (HGSOC), serous papillary, and endometrioid adenocarcinoma of ovary samples. The first column represents the stained images of the samples. The second to seventh columns show heatmaps corresponding to the mentioned TCs. (B) The heatmap illustrates the colocalization between two TC activities on spatial transcriptomic profiles from ovarian cancer samples. For each cell, the colocalization scores of the TCs at each of the three spatial transcriptomics samples OC 1, OC 2, and OC 3 are arranged in columns. A colocalization score of 4 between two TCs (red) indicates that the positively (+) and negatively (-) active regions of both TCs are perfectly colocalized. Conversely, a colocalization score of –4 between two TCs (blue) also indicates colocalization. Still, with inverse activity, i.e., the positively active regions of one TC are colocalized with the negatively active regions of the other TC or vice versa. A colocalization score close to 0 between two TCs (white) indicates that the activities of two TCs are spatially separated. The dashed and solid circles in the panel on the right side of the color bar represent two different TCs. Abbreviations: TC = transcriptional component.
Figure 6—figure supplement 1.
Spatial transcriptomic profiles in eight ovarian cancer samples.
(A) We employed a permutation-based approach to pinpoint the areas of significant TC activity in spatial transcriptomic profiles. We ran 5000 permutations for each TC-profile combination, yielding a p-value that indicates the extent to which the TC activity in the corresponding profile differs from what would be expected by chance (the null distribution). We then transformed these p-values into logarithmic values and represented them using a heatmap. Heatmaps of activity scores of the TCs are presented in individual rows for the high-grade serous ovarian cancer (HGSOC) samples. The first column represents the stained images of the samples. The second to seventh columns show heatmaps corresponding to the mentioned TCs. (B) The heatmap illustrates the colocalization between two TC activities on spatial transcriptomic profiles from ovarian cancer samples. For each cell, the colocalization scores of the TCs at each of the eight spatial transcriptomics samples are arranged in columns. A colocalization score of 4 between two TCs (red) indicates that the positively (+) and negatively (-) active regions of both TCs are perfectly colocalized. Conversely, a colocalization score of –4 between two TCs (blue) also indicates colocalization. Still, with inverse activity, i.e., the positively active regions of one TC are colocalized with the negatively active regions of the other TC or vice versa. A colocalization score close to 0 between two TCs (white) indicates that the activities of two TCs are spatially separated. The dashed and solid circles in the panel on the right side of the color bar represent two different TCs. Abbreviations: TC = transcriptional component.
Figure 6—figure supplement 2.
The activity scores of OS-associated TCs in different single-cell types from patients with HGSOC.
Abbreviations: TC = transcriptional component. OS = overall survival. HGSOC = high-grade serous ovarian cancer.
Discussion
In this study, we identified 374 TCs, each enriched for gene sets representing various biological processes in HGSOC samples. Six could stratify patients with HGSOC who had received platinum-based treatment into ten distinct OS groups.
The most significant TC in the survival tree analysis, TC121, captured a clinically relevant subtle transcriptional pattern linked to synaptic signaling not previously recognized in HGSOC. In the survival tree, TC121 identified 12% of the HGSOC patients with the shortest OS and, based on spatially resolved transcriptomic analyzed samples, is active in tumor regions. This observation supports the emerging role of neurons and neuronal projections as cancer hallmark-inducing constituents of the TME (Hanahan and Weinberg, 2011; Reavis et al., 2020; Gysler and Drapkin, 2021).
Further investigation on whether the activity of TC121 originated from tumor cells or in the TME revealed that the TC121 signal is coming from cells within the TME. The high activity of TC121 in low-grade glioma and glioblastoma multiforme patient samples (Figure 5B) is in agreement with the presence of neurons in large numbers within the TME of gliomas, where they form functional synapses with tumor cells (Radin and Tsirka, 2020; Venkatesh et al., 2019). Moreover, TC121 activity was lower in non-brain cancers, such as ovarian cancers, which contain fewer neurons and synapses in the TME compared to brain cancers. We expected TC121 activity to be low in the bulk transcriptomes of all cell lines, since they lack TME. TC121 activity in most cell lines, which includes glioblastoma and ovarian cancer cell lines, was indeed low. Neuroblastoma cell lines, however, exhibited high TC121 activity, which is likely due to retained synaptic formation capacity originating from neuroblast cells (De Preter et al., 2006; Mark et al., 2021). Lastly, TC121’s high activity observed in small, scattered regions within the tumor of spatially resolved transcriptomic ovarian cancer samples also supports TC121’s role in the TME.
TC121’s significant association with OS underscores the potential significance of synaptic signaling in HGSOC biology. Yet, the neuronal subtype and the molecular mechanisms associated with TC121 remain to be elucidated. A study in human ovarian cancer-bearing mice demonstrated that sympathetic innervation in HGSOC involves adrenergic signaling: norepinephrine released by sympathetic neurons binds to beta-adrenergic receptors on the cancer cells (Allen et al., 2018; Rains et al., 2017; Eng et al., 2014). This binding triggers the tumor cells to release brain-derived neurotrophic factor (BDNF), which enhances cancer innervation via activation of host neurotrophic receptor tyrosine kinase B receptors (NTRK2), thereby establishing a feed-forward loop of sustained signaling. BDNF and the nerve marker neurofilament protein expression were examined in 108 human ovarian tumors (De Preter et al., 2006). This study revealed that increased intratumoral nerve presence strongly correlates with elevated BDNF and norepinephrine levels, advanced tumor stage, and shorter OS in patients with ovarian cancer. This interaction can be targeted with pan-TRK inhibitors such as entrectinib and larotrectinib. Both drugs are showing promising results in multiple phase II trials, including ovarian cancer and breast cancer patients. Furthermore, a TRKB-specific inhibitor was developed (ANA-12), but has not been subjected to any clinical trials in cancer so far (Drilon et al., 2017; Ardini et al., 2016; Drilon et al., 2018; Burris et al., 2015). Our analysis indicated that BDNF is a prominent gene (with an absolute weight >3) in 10 TCs but not in TC121, suggesting that TC121 may indicate a distinct process unrelated to BDNF.
The significance of sensory innervation in HGSOC was evidenced by the co-localization of TRPV1, a marker for sensory neurons, and β-III tubulin, a general neuronal marker, in immunofluorescent staining of histological sections from 75 patients (Barr et al., 2021). Additionally, a murine model study employing neural tracing identified sensory neurons originating from local dorsal root ganglia and jugular–nodose ganglia, with axons extending into the TME (Barr et al., 2021). A transgenic murine model lacking nociceptors demonstrated that this specific subtype of sensory neurons was involved in tumor progression (Restaino et al., 2023). Another study showed that reducing the release of calcitonin gene-related peptide from tumor-innervating nociceptors could be a strategy to alleviate this effect of nociceptors by improving anti-tumor immunity of cytotoxic CD8 + T cells in a melanoma model bearing mice (Balood et al., 2022). This indicates that the signal from TC121 may represent an indirect influence on tumor cells via interactions with immune cells and the promotion of an immune suppressive TME. Furthermore, in cell lines derived from Trp53−/− Pten−/− murine HGSOC, the influence of nociceptors was characterized by the release of substance P (SP), their primary neuropeptide. SP is an alternative splicing product of the preprotachykinin A gene (TAC1) and binds to the receptor neurokinin 1 (NK1R), encoded by the TACR1 gene. NK1R expression was confirmed in the murine HGSOC cell line, and SP enhanced cellular proliferation in NK1R-positive murine HGSOC cancer cells in vitro (Restaino et al., 2023). Our analysis identified TAC1 and TACR1 as prominent genes in 15 and 2 TCs, respectively, yet not in TC121, and none of these TCs were associated with patient survival. Currently, there are no drugs specifically targeting tumor innervation in (ovarian) cancer (Li et al., 2022). Interestingly, the NK1R antagonist aprepitant effectively inhibited the metastasis-promoting effects of neural substance P in human breast and mammary cancer-bearing mice (Padmanaban et al., 2024), demonstrating the feasibility of such an approach. Strategies to disrupt neuronal signaling and neurotransmitter release in neurons target key elements of excitatory neurotransmission, such as calcium flux and vesicle formation. Drugs like ifenprodil and lamotrigine, commonly used to treat neuronal disorders, block glutamate release and subsequent neuronal signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine prevents synaptic vesicle formation (Williams, 2001; Reid et al., 2013). In vitro studies with HGSOC cell lines have demonstrated that ifenprodil significantly inhibits tumor proliferation, while reserpine induces apoptosis in cancer cells (Ramamoorthy et al., 2019; North et al., 2015). These approaches hold promise for inhibiting neuronal signaling and interactions in the TME. Therefore, it is essential that the mechanisms driving this nerve growth, the specifics of how nerves within the TME interact with ovarian cancer cells, and how they impact the survival of patients with HGSOC are further elucidated.
Altogether, the present study uncovered a clinically relevant TC linked to synaptic signaling not previously identified in HGSOC. This TC may represent a novel cancer cell-extrinsic mechanism within the TME, illustrating how cancer cells and nerve cells interact to promote enhanced proliferation. A deeper understanding of the molecular aspects of tumor innervation could pave the way for novel drug targets for patients with HGSOC.
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