Detection and characterization of lung cancer using cell-free DNA fragmentomes.
Dimitrios MathiosJakob Sidenius JohansenStephen CristianoJamie E MedinaJillian A PhallenKlaus R LarsenDaniel C BruhmNoushin NiknafsLeonardo FerreiraVilmos AdleffJia Yuee ChiaoAlessandro LealMichael NoeJames R WhiteAdith S ArunCarolyn HrubanAkshaya V AnnapragadaSarah Østrup JensenMai-Britt Worm ØrntoftAnders Husted MadsenBeatriz CarvalhoMeike de WitJacob CareyNicholas C DracopoliTara MaddalaKenneth C FangAnne-Renee HartmanPatrick M FordeValsamo AnagnostouJulie R BrahmerRemond J A FijnemanHans Jørgen NielsenGerrit A MeijerClaus Lindbjerg AndersenAnders MellemgaardStig Egil BojesenRobert B ScharpfVictor E VelculescuPublished in: Nature communications (2021)
Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.
Keyphrases
- genome wide
- papillary thyroid
- small cell lung cancer
- loop mediated isothermal amplification
- machine learning
- risk factors
- transcription factor
- squamous cell
- real time pcr
- label free
- randomized controlled trial
- dna methylation
- squamous cell carcinoma
- computed tomography
- lymph node metastasis
- gold nanoparticles
- childhood cancer
- magnetic resonance
- mass spectrometry
- dual energy
- pet ct