Victor Velculescu MD PhD: Ovarian Cancer Noninvasive Detection by Circulating DNA Fragmentome and Protein Biomarker AI Analysis

Audio Journal of Oncology Podcast

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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” 13:49secs

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-efficient approach (27) that is not confounded by clonal hematopoiesis (20, 28).

In this study, we present a method to detect ovarian cancer using cfDNA fragmentomes combined with protein biomarkers. This multianalyte combination has the benefit of utilizing genome-wide multifeature fragmentation analyses together with complementary protein biomarkers CA-125 and HE4 from the same blood draw that may have utility in both the screening and diagnostic settings.

Results

Clinical Cohorts

Blood samples in the discovery cohort were collected from women with ovarian cancer (n = 94), with benign adnexal masses (n = 203), or without any known ovarian lesions (n = 182), who were part of previously reported prospective diagnostic or screening efforts at hospitals in the Netherlands and Denmark (Table 1; Supplementary Table S1; refs. 9, 21, 23, 29). For the validation cohort, we analyzed samples from patients prospectively collected at the University of Pennsylvania or through a commercial source in the United States (n = 40 patients with ovarian cancer, n = 50 patients with benign ovarian masses, and n = 22 without known ovarian lesions; Table 1; Supplementary Table S1). The patients analyzed were largely representative of ovarian cancer subtypes, including high-grade serous (HGSOC), low-grade serous (LGSOC), clear cell, mucinous, and endometrioid ovarian cancers, across all International Federation of Gynecology and Obstetrics (FIGO) stages (Table 1).

NEWS RELEASE:

Artificial Intelligence Analysis of DNA Fragmentomes and Protein Biomarkers Noninvasively Detects Ovarian Cancer

April 9, 2024

SAN DIEGO – A blood-based machine learning assay that combines cell-free DNA (cfDNA) fragment patterns and levels of the proteins CA125 and HE4 could differentiate patients with ovarian cancer from healthy controls or patients with benign ovarian masses, according to a retrospective study presented at the American Association for Cancer Research (AACR) Annual Meeting 2024, held April 5-10.

Federal statistics list ovarian cancer as the fifth most common cause of cancer deaths among women in the United States, with a five-year survival rate of approximately 50%. Part of what makes ovarian cancer so deadly is that it does not typically cause symptoms in the early stages of disease, explained Jamie Medina, PhD, a postdoctoral fellow at the Johns Hopkins Kimmel Cancer Center.

“The lack of efficient screening tools, combined with the asymptomatic development of ovarian cancer, contributes to late diagnoses when effective treatment options are limited,” said Medina, who presented the study alongside co-first author Akshaya Annapragada, an MD/PhD student at the Johns Hopkins University School of Medicine. “A cost-effective, accessible detection approach could change clinical paradigms of ovarian cancer screening and potentially save lives.”

Liquid biopsy technologies, in which researchers analyze patients’ blood for evidence of tumor-derived DNA, have been explored as a way to noninvasively detect a variety of cancers; however, they have not always been useful in ovarian cancer, Medina explained. DELFI (DNA Evaluation of Fragments for early Interception), utilizes a newer method of liquid biopsy analysis, called fragmentomics, that has shown promise in improving the accuracy of such tests. The approach is based on detecting in the circulation changes in the size and distribution of cfDNA fragments across the genome, or the fragmentome.

“Because cancer cells are rapidly growing and dying and have chaotic genomes as compared to healthy cells, patients with cancer have different patterns of DNA fragments in their blood than patients without cancer,” Medina said. “By carefully analyzing these fragments across the entire human genome, we can detect subtle patterns indicating the presence of cancer.”

Medina, Annapragada, and colleagues analyzed fragmentomes from individuals with and without ovarian cancer using DELFI. They trained a machine learning algorithm to integrate the fragmentome data with plasma levels of two known biomarkers of ovarian cancer: the proteins CA125 and HE4.

“Ovarian cancer is an incredibly deadly disease with no great biomarkers for screening and early intervention,” said Victor Velculescu, MD, PhD, FAACR, senior author of the study, a professor of oncology, and codirector of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center. “Our goal was to overcome this challenge by combining genome-wide cell-free DNA fragmentation with protein biomarkers to develop a new high-performance approach for early detection of ovarian cancer.”

The researchers analyzed plasma from 134 women with ovarian cancer, 204 women without cancer, and 203 women with benign adnexal masses. They used the data to develop two models: one to examine

ovarian cancer screening in an asymptomatic population and the other to noninvasively differentiate benign masses from cancerous ones.

At a specificity of over 99% (nearly no false positives), the screening model identified 69%, 76%, 85%, and 100% of ovarian cancer cases staged I-IV, respectively; the area under the curve (a measure of accuracy that increases as the value approaches 1) was 0.97 across all stages, much higher than the performance of current biomarkers. For comparison, an analysis of CA125 levels alone identified 40%, 66%, 62%, and 100% of cases staged I-IV, respectively.

The diagnostic model was able to differentiate ovarian cancer from benign masses with an area under the curve of 0.87.

The group intends to validate their models in larger cohorts to strengthen the associations observed here, Velculescu said, but he found the current data encouraging. “This study contributes to a large body of work from our group demonstrating the power of genome-wide cell-free DNA fragmentation and machine learning to detect cancers with high performance,” he said. “Our findings indicate that this combined approach resulted in improved performance for screening compared to existing biomarkers.”

Limitations of this study include a relatively small sample size, a study population primarily comprised of American and European patients, and the retrospective nature of the analysis.

Funding for this study was provided by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up To Cancer, the Gray Foundation, The Honorable Tina Brozman Foundation, the Commonwealth Foundation, The Mark Foundation for Cancer Research, the COLE Foundation, Delfi Diagnostics, and the U.S. National Institutes of Health. Medina, Annapragada, and Velculescu are inventors on patent applications related to the use of cfDNA for cancer detection that have been submitted by Johns Hopkins University and licensed to Delfi Diagnostics. Velculescu is also an inventor on patent applications that have been submitted by Johns Hopkins University and licensed to Labcorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana, and ManaT Bio. Velculescu is a founder and member of the Board of Directors of Delfi Diagnostics, a company for which he owns stock and Johns Hopkins University owns equity. Velculescu is also an advisor to Virion Therapeutics and Epitope.

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