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.

Lung squamous cell carcinomas (LSCC) pathogenesis remains incompletely understood and biomarkers predicting treatment response remain lacking. Here we describe novel murine LSCC models driven by loss of Trp53 and Keap1, both of which are frequently mutated in human LSCCs. Homozygous inactivation of Keap1 or Trp53 promoted airway basal stem cell (ABSC) self-renewal, suggesting that mutations in these genes lead to expansion of mutant stem cell clones. Deletion of Trp53 and Keap1 in ABSCs, but not more differentiated tracheal cells, produced tumors recapitulating histological and molecular features of human LSCCs, indicating that they represent the likely cell of origin in this model. Deletion of Keap1 promoted tumor aggressiveness, metastasis, and resistance to oxidative stress and radiotherapy (RT). KEAP1/NRF2 mutation status predicted risk of local recurrence after RT in non-small lung cancer (NSCLC) patients and could be non-invasively identified in circulating tumor DNA. Thus, KEAP1/NRF2 mutations could serve as predictive biomarkers for personalization of therapeutic strategies for NSCLCs.

Circulating tumor cells (CTCs) are established cancer biomarkers for the “liquid biopsy” of tumors. Molecular analysis of single CTCs, which recapitulate primary and metastatic tumor biology, remains challenging because current platforms have limited throughput, are expensive, and are not easily translatable to the clinic. Here, we report a massively parallel, multigene-profiling nanoplatform to compartmentalize and analyze hundreds of single CTCs. After high-efficiency magnetic collection of CTC from blood, a single-cell nanowell array performs CTC mutation profiling using modular gene panels. Using this approach, we demonstrated multigene expression profiling of individual CTCs from non–small-cell lung cancer (NSCLC) patients with remarkable sensitivity. Thus, we report a high-throughput, multiplexed strategy for single-cell mutation profiling of individual lung cancer CTCs toward minimally invasive cancer therapy prediction and disease monitoring.

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