David M. Kurtz

TL;DR: This work introduces an approach for integrated digital error suppression (iDES), which combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules, and facilitates noninvasive variant detection across hundreds of kilobases of circulating tumor DNA.

TL;DR: This study shows that ctDNA analysis can robustly identify posttreatment MRD in patients with localized lung cancer, identifying residual/recurrent disease earlier than standard-of-care radiologic imaging, and thus could facilitate personalized adjuvant treatment at early time points when disease burden is lowest.

TL;DR: In this article, the authors employ CAPP-Seq ctDNA analysis to study resistance mechanisms in 43 non-small cell lung cancer (NSCLC) patients treated with the third-generation epidermal growth factor receptor (EGFR) inhibitor rociletinib.

TL;DR: The authors demonstrated that circulating tumor DNA in the patients’ blood is suitable for this analysis, allowing for periodic monitoring of each patient without repeated invasive biopsies and facilitating individualized therapy.

TL;DR: It is shown that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic, and a machine-learning method termed ‘lung cancer likelihood in plasma’ (Lung-CLiP) is developed, which can robustly discriminate early-Stage lung cancer patients from risk-matched controls.