High-throughput sequencing of circulating tumor DNA (ctDNA) promises to facilitate personalized cancer therapy. However, low quantities of cell-free DNA (cfDNA) in the blood and sequencing artifacts currently limit analytical sensitivity. To overcome these limitations, we introduce an approach for integrated digital error suppression (iDES). Our method combines in silico elimination of highly stereotypical background artifacts with a molecular barcoding strategy for the efficient recovery of cfDNA molecules. Individually, these two methods each improve the sensitivity of cancer personalized profiling by deep sequencing (CAPP-Seq) by ~3 fold, and synergize when combined to yield ~15-fold improvements. As a result, iDES-enhanced CAPP-Seq facilitates noninvasive variant detection across hundreds of kilobases. Applied to clinical non-small cell lung cancer (NSCLC) samples, our method enabled biopsy-free profiling of EGFR kinase domain mutations with 92% sensitivity and 96% specificity and detection of ctDNA down to 4 in 105 cfDNA molecules. We anticipate that iDES will aid the noninvasive genotyping and detection of ctDNA in research and clinical settings.

Identifying molecular residual disease (MRD) after treatment of localized lung cancer could facilitate early intervention and personalization of adjuvant therapies. Here, we apply cancer personalized profi ling by deep sequencing (CAPP-seq) circulating tumor DNA (ctDNA) analysis to 255 samples from 40 patients treated with curative intent for stage I–III lung cancer and 54 healthy adults. In 94% of evaluable patients experiencing recurrence, ctDNA was detectable in the fi rst posttreatment blood sample, indicating reliable identifi cation of MRD. Posttreatment ctDNA detection preceded radiographic progression in 72% of patients by a median of 5.2 months, and 53% of patients harbored ctDNA mutation profi les associated with favorable responses to tyrosine kinase inhibitors or immune checkpoint blockade. Collectively, these results indicate that ctDNA MRD in patients with lung cancer can be accurately detected using CAPP-seq and may allow personalized adjuvant treatment while disease burden is lowest.

Circulating tumour DNA (ctDNA) analysis facilitates studies of tumour heterogeneity. Here we 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. We observe multiple resistance mechanisms in 46% of patients after treatment with first-line inhibitors, indicating frequent intra-patient heterogeneity. Rociletinib resistance recurrently involves MET, EGFR, PIK3CA, ERRB2, KRAS and RB1. We describe a novel EGFR L798I mutation and find that EGFR C797S, which arises in ∼33% of patients after osimertinib treatment, occurs in <3% after rociletinib. Increased MET copy number is the most frequent rociletinib resistance mechanism in this cohort and patients with multiple pre-existing mechanisms (T790M and MET) experience inferior responses. Similarly, rociletinib-resistant xenografts develop MET amplification that can be overcome with the MET inhibitor crizotinib. These results underscore the importance of tumour heterogeneity in NSCLC and the utility of ctDNA-based resistance mechanism assessment.

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Patients with diffuse large B cell lymphoma (DLBCL) exhibit marked diversity in tumor behavior and outcomes, yet the identification of poor-risk groups remains challenging. In addition, the biology underlying these differences is incompletely understood. We hypothesized that characterization of mutational heterogeneity and genomic evolution using circulating tumor DNA (ctDNA) profiling could reveal molecular determinants of adverse outcomes. To address this hypothesis, we applied cancer personalized profiling by deep sequencing (CAPP-Seq) analysis to tumor biopsies and cell-free DNA samples from 92 lymphoma patients and 24 healthy subjects. At diagnosis, the amount of ctDNA was found to strongly correlate with clinical indices and was independently predictive of patient outcomes. We demonstrate that ctDNA genotyping can classify transcriptionally defined tumor subtypes, including DLBCL cell of origin, directly from plasma. By simultaneously tracking multiple somatic mutations in ctDNA, our approach outperformed immunoglobulin sequencing and radiographic imaging for the detection of minimal residual disease and facilitated noninvasive identification of emergent resistance mutations to targeted therapies. In addition, we identified distinct patterns of clonal evolution distinguishing indolent follicular lymphomas from those that transformed into DLBCL, allowing for potential noninvasive prediction of histological transformation. Collectively, our results demonstrate that ctDNA analysis reveals biological factors that underlie lymphoma clinical outcomes and could facilitate individualized therapy.