Daria Afenteva

Background: Antibody-drug conjugates (ADCs) represent a promising precision medicine approach for difficult-to-treat cancers, yet the spatial, temporal, and cellular heterogeneity of clinically approved ADC targets in ovarian high-grade serous carcinoma (HGSC) remains incompletely defined. Methods: We analyzed bulk RNA-sequencing, single-cell RNA-sequencing, and whole-genome sequencing data from 867 samples across 304 patients enrolled in the real-world, observational DECIDER cohort to systematically evaluate 11 approved ADC targets. Results: Here we show that FOLR1 , TACSTD2 , and ERBB2 emerged as highly expressed candidates. Inter-patient variability exceeded intra-patient heterogeneity, which further decreased after neoadjuvant chemotherapy. Target expression was highly concordant across anatomical sites and largely stable from diagnosis to relapse. Single-cell RNA-sequencing results revealed that TACSTD2 and FOLR1 showed the most frequent cancer cell-restricted expression. In rare cases of gene amplification, ERBB2 and F3 emerged as potential targets alongside TACSTD2 and FOLR1 . Overall, 80% of patients displayed homogeneous expression of at least one actionable target, with frequent co-expression of TACSTD2 and FOLR1 . Conclusions: These findings indicate that ADC target expression in HGSC is broadly stable across space and time and support the prioritization and strategic integration of TACSTD2 - and FOLR1 -directed ADCs.

Understanding how epigenetic mechanisms shape metastatic dissemination and therapy response in ovarian high-grade serous carcinoma (HGSC) has been hindered by the absence of large, anatomically diverse, and longitudinal datasets. Here, we present a comprehensive resource of the HGSC methylome, comprising 404 whole-genome bisulfite sequenced samples from 125 patients belonging to the real-world DECIDER trial. Tumor-specific methylomes were integrated with matched RNA sequencing, whole-genome sequencing and clinical outcomes, which enabled identification of > 8,500 regulatory promoters. Our results show that the regulatory methylome is largely established early in tumor progression and remains stable through chemotherapy. Tumors with poor response to treatment are marked by widespread promoter hypermethylation, particularly cancer cells in ascites, leading to epigenetic silencing of pathways targeted by chemotherapy agents. Our proof-of-principle analysis shows that these epigenetic signatures are detected in plasma-derived DNA, highlighting the value of DNA methylation in liquid biopsy-based disease monitoring. This study provides a foundational resource for dissecting the spatial and temporal dynamics of cancer methylomes and establishes a framework for integrating epigenetic states with clinical trajectories in HGSC.

Supp. Fig. S1 shows signature robustness. Supp. Fig. S2 displays the association of all signatures with homologous recombination and BRCA1/2 mutations. Supp. Fig. S3 depicts the association of signatures with whole genome duplication, amplification of CCNE1, KRAS, MYC, and MECOM and the relationship with survival. Supp. Fig. S4 shows how complex structural variants manifest and in which signatures they are prevalent. Supp. Fig. S5 explains the SCN-G segmentation aberration. Supp. Fig. S6 presents the validation dataset results in terms of survival and genomic characteristics. Supp. Fig. S7 displays transcriptomics analysis results for the proliferative HRP subtype. Supp. Fig. S8 depicts the transcriptomic results for the EMTHRP subtype. Supp. Fig. S9 contains the results from the transcriptomic analysis for the core HRP subtype.

Summary of biological associations and characteristics attributed to each signature.

Subtype discovery. The SCN signature activity profiles have been clustered into six subtypes with ConsensusClusterPlus and annotated in different colors beneath the dendrogram. The accompanying barplot illustrates signature activity values, with each color representing a distinct signature. Annotations below the main barplot include mutational HR status and the presence of specific gene alterations, such as CDK12, BRCA1, and BRCA2 mutations, as well as CCNE1, MYC, and AKT2 amplifications. A heatmap also displays the counts of complex structural variations (SV) using a color-coded scale. The purple bar plot below indicates the number of breakpoints, the red track with light blue dots represents the tumor mutational burden (TMB), and the amount of long interspersed nuclear elements (LINE) in each sample is shown as a heatmap in the bottom annotation. Notably, AKT2 amplification and CDK12 mutation are mutually exclusive to HRD clusters, whereas CCNE1 and MYC amplifications are present in both HRP and HRD compartments. Moreover, HRD subtypes and the proliferative HRD subtype are characterized by high TMB and number of breaks, and the proliferative subtype is enriched in pyrgos and chromoplexy. Further visualization of the clusters is available at GenomeSpy_6.

Study and signature overview. A, Schematic summary of the data and integration workflow. B, Feature summary: median of the value of each feature in the cohort, with error bars representing the quantiles 0.25 and 0.75. The features are colored according to the feature class and displayed in the boxes above the distribution. Feature classes derived from continuous features are connected below, with their distributions transformed on log scale. Colors match classes in A. The red dashed lines represent discretization thresholds determined using the Jenks natural breaks algorithm. CNV, copy number variant; LINE, long interspersed nuclear elements; SV, structural variant. C, Feature profiles of extracted signatures, derived with SigProfilerExtractor and using the features in A. The signatures exhibit distinct patterns, emphasizing their differences, and can be further observed in the interactive visualization at GenomeSpy_2.

Graphical and textual explanation of the features used for the summarization of genomic profiles.

Associated publication for each single-cell RNA sample.

Biological and clinical associations of the SCN signatures. A, Correlation plot between COSMIC mutational and extracted signatures. Cell color indicates correlation strength and direction. Significance is assessed using Spearman’s correlation with FDR correction. B, Distribution of signature SCN-E (top) and SCN-A (bottom). Each panel displays the signature activity across HRP samples, HRD samples without the respective BRCA mutation (BRCA1 for SCN-E and BRCA2 for SCN-A), and samples with the corresponding BRCA mutation. Samples with BRCA1 deficiency exhibit significantly higher SCN-E signature activity, whereas BRCA2-mutated samples show the same trend with SCN-A signature activity. Interactive visualization is available at GenomeSpy_3. C, Kaplan–Meier survival analysis of HRD-related signatures with log-rank P-values. Signature activities are first summarized per patient using the sample median. Patients are then divided into high and low signature activities using the median signature activity among all patients as a threshold. Elevated HRD-related signature activity is associated with improved treatment response. PFI, platinum-free interval. D, SCN-F signature activities for patients bearing CDK12 mutation and patients not having it. Mann–Whitney U-test with FDR correction significance level is reported (P = 8.49e−10). E, Correlation between copy number alterations and signature activity. Bar plots display the number of amplifications (left), gains (middle), and deletions (right) derived from GISTIC, whose copy number–ploidy ratio correlates with the activity of each SCN signature. The length of each bar segment represents the number of copy number events in each correlation category. SCN-I and SCN-J display the highest number of positive correlations with amplified regions. Similarly, SCN-C exhibits a high number of positive correlations with gains and deletions, excluding HRD-related signatures. In contrast, SCN-K exhibits negative correlations with most deletion and gain peaks. F, Difference in breakpoints abundance in chromosome 19 between CCNE1amp samples and normal CCNE1 samples. The counts are referred to bins of 0.5 Mb. G, GenomeSpy visualization of the signature SCN-K displaying the segmentation profiles of the five samples with the highest value for SCN-K activity, also available at GenomeSpy_5.