DELFI Detects What Others Cannot

We are creating next-generation liquid biopsies fueled by breakthroughs in genomics and machine learning to revolutionize early cancer detection.

Unlocking a Vast Trove of Diagnostic Data

Our unique, genome-wide fragmentomic-based approach to early detection isn’t limited to the small number of mutations or alterations detected by other liquid biopsies, which are just pieces of a much larger picture.

Unique Advanced Machine Learning Engine

DELFI’s advanced machine learning technology derives its analytical power by comparing an individual’s cell-free DNA patterns against populations with and without cancer. The technology uses millions of data points to identify both the presence of cancer and its tissue of origin. 

Higher Performance Than Other Methods

By examining cell-free DNA fragments across the genome, DELFI’s machine learning system analyzes orders of magnitude more data on the presence or absence of cancer than possible with conventional technologies that look for limited changes in DNA sequences, methylation, or proteins. DELFI’s highly accurate assays are not affected by confounding conditions such as clonal hematopoiesis of indeterminate potential (CHIP) or other diseases.

A Growing Body of Evidence

First published in Nature in 2019, DELFI’s methods continue to demonstrate the potential of using this technology to develop high-performing, accessible liquid biopsy tests.

PUBLICATIONS

Our research and scientific findings in respected peer-reviewed publications and events

Literature

Clinical validation of a cell-free DNA fragmentome assay for augmentation of lung cancer early detection
Mazzone, P.J., Bach, P.B., Carey, J., et al. Cancer Discovery. 10.1158 – 2159-8290. CD-24-0519. (2024).

Single-molecule genome-wide mutation profiles of cell-free DNA for non-invasive detection of cancer
Bruhm, D.C., Mathios, D., Foda, Z.H. et al. Nature Genetics. 10.1038 – s41588-023-01446-3 (2023)

Cell-free DNA fragmentomes in the diagnostic evaluation of patients with symptoms suggestive of lung cancer
Leal, A., Mathios, D., Jakubowski, D., et al. Chest. 10.1016 – 2023.04.033 (2023)

Detecting liver cancer using cell-free DNA fragmentomes. Foda Z., Annapragada A., Boyapati, K., et al. Cancer Discovery. CD-22-0659 (2022).

Detection and characterization of lung cancer using cell-free DNA fragmentomes. Mathios, D., Johansen, J.S., Cristiano, S. et al. Nat Commun. 12, 5060 (2021).

White blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer.
Leal A, van Grieken NCT, Palsgrove DN, et al.
Nat Commun. 2020;11(1):525.

Genome-wide cell-free DNA fragmentation in patients with cancer. Cristiano S, Leal A, Phallen J, et al. Nature. 2019;570(7761):385-389.

Presentations

Cell-free DNA fragmentation profiling for monitoring therapeutic response in metastatic colorectal cancer. Alipanahi B, van ‘t Erve I, Lumbard K, et al. Presented at AACR Meeting: April 14-19, 2023.

Prospective evaluation of cell-free DNA fragmentomes for lung cancer detection. Mazzone P, Wong K, Tsay J, et al. Presented at AACR Meeting: April 14-19, 2023.

CASCADE-LUNG: Validation of a blood-based assay that evaluates cell-free DNA fragmentation patterns to detect lung cancer. Barta J, Freedland S, Mazzone P et al. Presented at AACR Meeting: April 14-19, 2023.

DELFI as a Real-Time Treatment Response Assessment for Patients with Cancer. Presented at AACR Meeting Online: April 8-13, 2022.

Genome-wide cfDNA fragmentation in patients with cancer and other diseases. Carey J, Jones S, Leal A, et al. Presented at 2021 ASCO Annual Meeting Online: June 4–8, 2021.

Modeling cell-free DNA fragment size densities for non-invasive detection of cancer. Carey J, Chesnick B, Butler D, Rongione M, Parmigiani G, Velculescu VE, Dracopoli NC, Scharpf RB. Presented at 2021 ASCO Annual Meeting Online: June 4–8, 2021.

Detecting cancer using genome wide cfDNA fragmentation in a prospective diagnostic cohort.
Carey J, Leal A, Chesnick B, et al. Presented at
Virtual AACR Annual Meeting Week 1: April 10-15, 2021.