Abstract The properties of cancer-associated genetic changes in cell-free DNA (cfDNA) are not fully understood. We performed whole-genome sequencing (WGS) of cfDNA as well as tumor tissue and white blood cells (WBCs) from 1,807 samples of 1,064 patients across eight common cancer types. Characterization of single base substitutions, small insertions and deletions, structural variants (SVs), and phased variants in single cfDNA molecules revealed unique properties of tumor-derived alterations as well as differences in error rates that spanned orders of magnitude. Given the low error rate associated with detection of tumor-specific rearrangement junctions in cfDNA, we hypothesized that these types of changes could enable detection of circulating tumor DNA (ctDNA) without prior knowledge of the alterations in the tumor tissue. As an example of this approach, we scanned each sequenced fragment genome-wide in cfDNA samples from the CheckPAC trial of patients with metastatic pancreatic cancer treated with radiation and immunotherapy to identify putative rearrangement junctions. We identified 22,010,911 such fragments but only 1,572 (0.007%) and 58,339 (0.27%) of these were present in the matched tumor or WBC samples, respectively, with the remaining identified only in cfDNA. We characterized each cfDNA fragment by the SV type, SV size, microhomology and insertion at the breakpoint junction, fragment size, and the location of the breakpoint with respect to the nearest fragment end, identifying differences depending on the origin of the SV. Machine learning analyses of SVs from cfDNA resulted in a high cross-validated performance for detection of tumor-specific SVs with an area under the curve (AUC) of 0.97 (95% CI: 0.97-0.98). After enriching for fragments most likely to be tumor-derived, we found that the number of cfDNA fragments containing SVs was highly correlated with the number obtained using a tumor-informed approach (Pearson correlation coefficient = 0.87, p<0.001), and could recapitulate longitudinal ctDNA levels and clinical outcomes using only low-coverage (∼4x) plasma WGS. The universal nature of tumor-associated sequence and structural alterations in cfDNA may be broadly useful for cancer detection. Citation Format: Daniel C. Bruhm, Carolyn Hruban, Adrianna L. Bartolomucci, Akshaya V. Annapragada, Sarah Short, Shashikant Koul, Kaui P. Lebarbenchon, Julia S. Johansen, Inna M. Chen, Andrei Sorop, Razvan Iacob, Speranta Iacob, Liana Gheorghe, Simona Dima, Katherine A. McGlynn, Manuel Ramírez-Zea, John Groopman, PLCRC-MEDOCC group, Remond J. Fijneman, Gerrit A. Meijer, Zachariah H. Foda, Jillian Phallen, Robert B. Scharpf, Victor E. Velculescu. Sequence and structural DNA alterations in the circulation of patients with cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2591.

Supplementary Table S1. Summary of clinical information. Supplementary Table S2. Summary of sample characteristics. Supplementary Table S3. Summary of whole-genome sequencing and fragment analyses. Supplementary Table S4. Summary of protein analyses. Supplementary Table S5. Summary of machine learning models and scores. Supplementary Table S6. Summary of performance for ovarian cancer detection.

Rearrangements of ovarian endometrioid and ovarian mucinous carcinomas identified by TRELLIS. Rearrangements present in ovarian endometrioid carcinomas CGOV161T and CGOV172T (a, b) and an ovarian mucinous tumor CGOV173T (c). Split reads that span the fusion junction are shown in black, while read pairs that reside on either side of the junction are shown in green and blue.

Relationship between tumor purity and number of somatic mutations. (a) Tumor purity for multiple histological tumor types were estimated using FACETS. Samples that FACETS did not process due to undetectable copy number changes are marked with an x. (b) Patients with uterine endometrioid adenocarcinomas were more than twice as likely to have a hypermutator defect compared to patients with other cancers, including patients with ovarian endometrioid cancer (95% CI: 1.1 - 4.2-fold increase).

Somatic sequence alterations from whole genome analyses.

Amplifications from whole genome analyses.

Integrated genomic analyses of ovarian and colorectal mucinous cancers. Integrated analyses of somatic point mutations, structural variants, including linked amplicons, deletions, and rearrangements, organized by pathways. The top of the figure shows the number of each type of alteration, the middle portion reflects the alterations present in each sample for each gene and pathway, and the bottom shows the mutation spectra.

Supplementary Figure S1. Evaluation of screening model DELFI-Pro scores and comorbidities in individuals without cancer. Supplementary Figure S2. DELFI-Pro score evaluation in available clinical characteristics of patients with cancer. Supplementary Figure S3. Stability analysis across fold repeats and collection source. Supplementary Figure S4. Detection of ovarian cancer subtypes using DELFI-Pro screening model. Supplementary Figure S5. ROC analyses of asymptomatic individuals in the using screening or diagnostic models in the Discovery Cohort. Supplementary Figure S6. Performance of ichorCNA and median cfDNA fragment lengths in the Discovery Cohorts. Supplementary Figure S7. Detection of ovarian cancer subtypes using DELFI-Pro at high specificity. Supplementary Figure S8. Genome-wide fragmentation profiles are altered in patients in the Validation Cohort with ovarian cancer. Supplementary Figure S9. Analyses of chromosomal changes in Discovery and Validation cohorts. Supplementary Figure S10. Performance of DELFI-Pro for detection of ovarian cancer in the Validation Cohort. Supplementary Figure S11. Correlation of rank ordered DELFI-Pro scores for the Screening and Diagnostic models. Supplementary Figure S12. Assessment of DELFI-Pro in women with benign lesions. Supplementary Figure S13. Performance of DELFI-Pro for distinguishing ovarian cancer from benign masses. Supplementary Figure S14. Performance of DELFI-Pro for distinguishing ovarian cancer subtypes from benign masses. Supplementary Figure S15. Performance of DELFI-Pro for distinguishing ovarian cancer subtypes from benign masses in Validation Cohort. Supplementary Figure S16. Evaluation of overall tumor burden using the sum of reported lesion diameters. Supplementary Figure S17. Evaluation of CA1-25 and HE4 blood concentrations measured at different centers.

Heatmap of methylation values in mucinous and endometrial histotypes. (a) Methylation levels (Betas) at 945 CpG sites having the highest variance across 164 TCGA patient samples that included 77 colorectal mucinous tumors, 37 stomach mucinous tumors, and 46 uterine endometrial samples from normal tissue. (b) Methylation levels at the same CpG sites were obtained from 16 patients with ovarian mucinous carcinomas, 8 patients with ovarian endometrioid carcinomas, 5 patients with stomach mucinous carcinomas, 6 patients with pancreatic mucinous carcinomas, and 1 patient with colorectal mucinous carcinoma.

Circos plots of uterine endometrioid carcinoma samples.

Circos plots of colorectal mucinous carcinoma samples.

Mutation signature analyses. (a) Mutation signatures for ovarian and uterine endometrioid carcinomas visualized with unsupervised clustering. (b) Mutation signatures for ovarian and GI mucinous carcinomas. The intensity of the heatmap colors indicates the percentage of the overall mutational profile for a sample that is explained by the mutation signature.

Summary of ovarian, uterine, and GI tumors analyzed through whole exome and whole genome analyses.

Summary of next-generation sequencing analyses.

Abstract Ovarian cancer is a leading cause of death for women worldwide, in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker [cancer antigen 125 (CA-125) and human epididymis protein 4 (HE4)] analyses to evaluate 591 women with ovarian cancer, with benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivities of 72%, 69%, 87%, and 100% for stages I to IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100%, and HE4 alone detected 28%, 27%, 67%, and 100% of ovarian cancers for stages I to IV, respectively. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC = 0.88, 95% confidence interval, 0.83–0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation. Significance: There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

Integrated genomic analyses of ovarian and uterine endometrioid adenocarcinomas. Integrated analyses of somatic point mutations, structural variants, including linked amplicons, deletions, and rearrangements, organized by pathways. The top of the figure shows the number of each type of alteration, the middle portion reflects the alterations present in each sample for each gene and pathway, and the bottom shows the mutation spectra.

Circos plots of ovarian mucinous carcinoma samples.

WGS analyses of linked amplicons and rearrangements. A, Circos plots for linked amplicons for individual tumor samples with >100 structural variants. Circos plots depict copy number alterations as well as intra- and inter-chromosomal rearrangements. Segmented normalized estimates of read depth were used to identify candidate focal amplifications (teal) and focal deletions (red). Rearranged read pairs and split reads were used to identify inter- and intra-chromosomal rearrangements (blue). B, For linked amplicon graphs, nodes represent genes, node size represents the number of times the segment is represented in the genome, color indicates the chromosome location of the amplified segment, and edges denote segments that are connected in the rearranged cancer genome. Known driver genes indicated by triangles are often hubs in these networks, suggesting that amplification of these genes occurs early and can be connected to many of the other amplicons. C, Reads aligned to the fusion junction of two representative rearrangements. Split reads that span the fusion junction are shown in black, whereas read pairs that reside on either side of the junction are shown in green and blue, providing additional evidence for specific rearrangements.

Proportion of methylated CpG sites in ovarian and mucinous carcinomas. The proportion of methylated CpG sites (mean Beta-values >0.3) are shown for patients with mucinous stomach, mucinous pancreatic, and ovarian mucinous and ovarian endometrioid carcinomas. Methylation was only available for one individual with colorectal mucinous carcinoma (not shown).

Somatic sequence alterations from whole exome analyses.