# Evaluation of cell-free DNA approaches for multi-cancer early detection

Source: https://onco.cc/key-papers/paper-jamshidi-ccga1-cfdna-approaches-cancer-cell-2022/  
OnCo record `paper-jamshidi-ccga1-cfdna-approaches-cancer-cell-2022` (Key paper). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

GRAIL compared ten ways of reading free-floating DNA in the same blood samples and found that methylation worked best both for spotting cancer and for saying where it was.

## Summary

In the first substudy of the Circulating Cell-free Genome Atlas (NCT02889978), ten machine-learning classifiers built on different cell-free DNA features were trained on the same samples and independently validated. At 98% specificity, whole-genome methylation, single-nucleotide variants with white-blood-cell background removal, and combined scores gave the highest cancer signal sensitivity. The circulating tumour allele fraction predicted classifier performance better than clinical stage or tumour type, and whole-genome methylation best predicted cancer signal origin, which informed the targeted methylation test.

## Fields

- Kind: Key paper
- Last checked: 2026-10-29
- Journal: Cancer Cell
- Year: 2022
- DOI: 10.1016/j.ccell.2022.10.022
- Authors: Jamshidi A, Liu MC, Klein EA, et al.
- Findings: Whole-genome methylation among the most sensitive approaches at 98% specificity.; Tumour allele fraction in blood predicted detection better than stage.; Methylation best predicted cancer signal origin.
- What it means: Whether a cancer is found depends mostly on how much tumour DNA it sheds into the blood, which is why small early cancers are often missed by every approach.
- Caveats: Case-control samples; comparisons are within one company's platform.; No absolute sensitivity figures in the abstract.

## Sources

- Cancer Cell 2022: CCGA first substudy: https://doi.org/10.1016/j.ccell.2022.10.022
- PubMed: https://pubmed.ncbi.nlm.nih.gov/36400018/

## Connected records

- fronts: [Early Detection & Screening](https://onco.cc/fronts/early-detection/)
- technologies: [Cell-free DNA methylation tests](https://onco.cc/technologies/cfdna-methylation-testing/), [cfDNA fragmentomics](https://onco.cc/technologies/fragmentomics/), [Multi-cancer early detection (MCED)](https://onco.cc/technologies/mced/)
- drugs: [Galleri](https://onco.cc/drugs/galleri/)
- companies: [GRAIL](https://onco.cc/companies/grail/)
- terms: [Cell-free DNA (cfDNA)](https://onco.cc/terms/cfdna/), [Circulating tumour DNA (ctDNA)](https://onco.cc/terms/ctdna/)
- trials: [CCGA (Circulating Cell-free Genome Atlas)](https://onco.cc/trials/ccga/)
- people: [Eric A. Klein](https://onco.cc/people/eric-klein/)
- journals: [Cancer Cell](https://onco.cc/journals/cancer-cell/)

---
JSON: https://onco.cc/api/v1/entities/paper-jamshidi-ccga1-cfdna-approaches-cancer-cell-2022.json