An interview with:

Victor Velculescu MD PhD, Co-director, Cancer Genetics & Epigenetics Program, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.

SAN DIEGO, USA—A blood test using an artificial intelligence DNA pattern recognition system that brings earlier, more certain detection of ovarian cancer was reported at the American Association for Cancer Research Annual Meeting held in San Diego.

The test analyses patterns of fragments of circulating DNA (called DNA fragmentomes). When combined with analysis of circulating tumor protein markers these were found to be highly correlated with ovarian cancer. The test uses the DELFI (DNA Evaluation of Fragments for early Interception) system that has already been established for early detection of lung cancer.

At the San Diego conference lead author of the research, Victor Velculescu MD PhD, Co-director, Cancer Genetics & Epigenetics Program, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland discussed the findings with Peter Goodwin.

AUDIO JOURNAL OF ONCOLOGY with: Victor Velculescu MD PhD. IN: “Hello, Peter Goodwin here …..OUT:  ……..for the Audio Journal of Oncology, I’m Peter Goodwin”   13:49 secs

2024 AACR ABSTRACT:

Abstract 773: Early detection of ovarian cancer using cell-free DNA fragmentomes 

AUTHORS:

Akshaya V. Annapragada; Jamie E. Medina; Victor E. Velculescu et al.

https://aacrjournals.org/cancerres/article/83/7_Supplement/773/719566/Abstract-773-Early-detection-of-ovarian-cancer

https://pubmed.ncbi.nlm.nih.gov/39345137/

Early Detection of Ovarian Cancer Using Cell-Free DNA Fragmentomes and Protein Biomarkers

Jamie E Medina # 1, Akshaya V Annapragada # 1, Pien Lof 2, Sarah Short 1, Adrianna L Bartolomucci 1, Dimitrios Mathios 1, Shashikant Koul 1, Noushin Niknafs 1, Michaël Noë 1, Zachariah H Foda 1, Daniel C Bruhm 1, Carolyn Hruban 1, Nicholas A Vulpescu 1, Euihye Jung 3, Renu Dua 1, Jenna V Canzoniero 1, Stephen Cristiano 1, Vilmos Adleff 1, Heather Symecko 4, Daan van den Broek 5, Lori J Sokoll 1, Stephen B Baylin 1, Michael F Press 6, Dennis J Slamon 7, Gottfried E Konecny 7, Christina Therkildsen 8, Beatriz Carvalho 9, Gerrit A Meijer 9, Claus Lindbjerg Andersen 10 11, Susan M Domchek 3 4, Ronny Drapkin 3 4, Robert B Scharpf 1, Jillian Phallen 1, Christine A R Lok 2, Victor E Velculescu 1

Affiliations

• 1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.


• 2Department of Gynecologic Oncology, Centre of Gynecologic Oncology Amsterdam, Antoni van Leeuwenhoek Hospital-The Netherlands Cancer Institute, Amsterdam, the Netherlands.


• 3Penn Ovarian Cancer Research Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.


• 4Basser Center for BRCA, Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania.


• 5Department of Laboratory Medicine, Antoni van Leeuwenhoek Hospital-The Netherlands Cancer Institute, Amsterdam, the Netherlands.


• 6Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, California.


• 7David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.


• 8Department of Surgical Gastroenterology, Hvidovre Hospital, Hvidovre, Denmark.


• 9Department of Pathology, Antoni van Leeuwenhoek Hospital-The Netherlands Cancer Institute, Amsterdam, the Netherlands.


• 10Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark.


• 11Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

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.

Introduction

Ovarian cancer is a leading cause of death in women worldwide, with more than 300,000 new cases and nearly 200,000 deaths globally each year (1). In the United States during 2024, approximately 19,600 new cases will be diagnosed and 12,700 women will die from ovarian cancer (2). The most common form of ovarian cancer is epithelial ovarian cancer, which comprises four major subtypes: serous, clear cell, mucinous, and endometrioid carcinomas. According to the Surveillance, Epidemiology, and End Results database, for individuals with detected invasive epithelial ovarian cancer, the estimated 5-year survival is 93% and 75% for localized (stage I) or regional (stage II or stage IIIA1 with regional lymph node involvement) disease, respectively, compared with 31% for distant disease (remaining stage III or stage IV; refs. 3, 4). Unfortunately, ovarian cancer is usually detected in advanced stages (stages III and IV) due to nonspecific clinical symptoms at earlier stages and the lack of an effective screening approach (3). Consequently, there is a clear unmet clinical need for the development of highly specific and sensitive assays to detect ovarian cancer in its earliest stages.

Ovarian cancer screening trials such as the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (5), the U.K. Collaborative Trial of Ovarian Cancer Screening (UKCTOCS; ref. 6), and the Normal Risk Ovarian Screening Study (ref. 7) have shown that existing biomarkers, including cancer antigen 125 (CA-125), may provide a shift toward detection of earlier stages of cancer but not a survival benefit, likely because of suboptimal detection of ovarian cancers. These analyses open the door to new and more effective approaches aimed at identifying combinations of biomarkers with improved performance for early ovarian cancer detection. Such approaches would need to be affordable, accessible, and have high performance for high-grade serous ovarian carcinoma (HGSOC), which is more aggressive, typically detected in late stages, and responsible for the majority of ovarian cancer deaths (8).

A secondary clinical need also exists in determining whether women presenting with ovarian masses have benign or malignant lesions. In this setting, preoperative malignancy classification is challenging and may lead to unnecessary procedures. A number of biomarkers have been proposed in this setting, including CA-125 and human epididymis protein 4 (HE4; refs. 9–11). Prediction models using a combination of multiple protein biomarkers as well as age and menopausal status (12), the risk of malignancy index (ref. 13), and other ultrasound classifications (International Ovarian Tumor Analysis; ref. 14) have been developed, but these vary in accuracy, performance, and ease of use in a clinical setting.

Analyses of circulating cell-free DNA (cfDNA) provide another approach for early cancer detection in the screening or diagnostic settings. Approaches for ovarian cancer have included identification of tumor-specific mutations (15, 16), or alterations in DNA methylation (17), or specific repeat sequences (18, 19); however, these approaches have had limited sensitivities for early-stage disease, may be confounded by alterations in white blood cells (20), and have not been validated for clinical use. An emerging approach of cfDNA analyses have focused on the “cfDNA fragmentome,” defined as the genome-wide compendium of cfDNA fragments in the circulation, providing an integrated view of the chromatin, genome, epigenome, and transcriptome states of normal and cancer cells of an individual. Recent cfDNA fragmentome analyses using low-coverage whole-genome sequencing (WGS) combined with machine learning using DNA evaluation of fragments for early interception (DELFI) have demonstrated high sensitivity for early detection across lung (21), liver (22), and other cancer types (23–26) using an accessible, cost-efficie