Open AccessCombining variant detection and fragment length analysis improves detection of minimal residual disease in postsurgery circulating tumour DNA of stage II–IIIA NSCLC patients
Author(s) -
Vessies Daan C. L.,
Schuurbiers Milou M. F.,
Noort Vincent,
Schouten Irene,
Linders Theodora C.,
Lanfermeijer Mirthe,
Ramkisoensing Kalpana L.,
Hartemink Koen J.,
Monkhorst Kim,
Heuvel Michel M.,
Broek Daan
Publication year - 2022
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1002/1878-0261.13267
Subject(s) - minimal residual disease , stage (stratigraphy) , medicine , oncology , adjuvant , biology , paleontology , leukemia
Stage II–IIIA nonsmall cell lung cancer (NSCLC) patients receive adjuvant chemotherapy after surgery as standard‐of‐care treatment, even though only approximately 5.8% of patients will benefit. Identifying patients with minimal residual disease (MRD) after surgery using tissue‐informed testing of postoperative plasma circulating cell‐free tumour DNA (ctDNA) may allow adjuvant therapy to be withheld from patients without MRD. However, the detection of MRD in the postoperative setting is challenging, and more sensitive methods are urgently needed. We developed a method that combines variant calling and a novel ctDNA fragment length analysis using hybrid capture sequencing data. Among 36 stage II–IIIA NSCLC patients, this method distinguished patients with and without recurrence of disease in a 20 times repeated 10‐fold cross validation with 75% accuracy ( P = 0.0029). In contrast, using only variant calling or only fragment length analysis, no signification distinction between patients was shown ( P = 0.24 and P = 0.074 respectively). In addition, a variant‐level fragmentation score was developed that was able to classify variants detected in plasma cfDNA into tumour‐derived or white‐blood‐cell‐derived variants with 84% accuracy. The findings in this study may help drive the integration of various types of information from the same data, eventually leading to cheaper and more sensitive techniques to be used in this challenging clinical setting.