Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling

Abstract

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 profiling 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 first posttreatment blood sample, indicating reliable identification 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 profiles 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.Significance: 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. Cancer Discov; 7(12); 1394-403. ©2017 AACR.See related commentary by Comino-Mendez and Turner, p. 1368This article is highlighted in the In This Issue feature, p. 1355.

Conflict of interest statement

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed by the other authors.

©2017 American Association for Cancer Research.

Figures

Figure 1

Pretreatment assessment of ctDNA in patients with localized lung cancer. A, Study schematic. Patients with biopsy- and imaging-proven nonmetastatic lung cancer were enrolled pretreatment. Plasma samples were collected before treatment and at follow-up visits, which occurred every 3–6 months and were usually coincident with surveillance scans (CT or PET/CT). B, Co-mutation plot based on pretreatment ctDNA analysis of patients with localized lung cancer. Each column represents pretreatment data from a single patient. Mutant allele fraction is shown in the top bar graph. Top heat maps indicate key patient characteristics. Mutation recurrence rate is depicted by bar graph to the right. Nonsynonymous mutations in candidate driver genes are shown in descending order of prevalence in the middle heat map. The number of other (i.e., likely passenger) mutations detected is indicated in the bottom heat map. C, Pie chart showing the number of candidate driver and other mutations detected in pretreatment plasma. D, ROC analysis of pretreatment (n = 40) and healthy control (n = 54) plasma samples using candidate driver, other, or both types of mutations. E, Scatter plot correlating ctDNA concentration (haploid genome equivalents per mL, hGE/mL) with pretreatment metabolic tumor volume (MTV) measured by PET-CT in patients with detectable ctDNA (n = 37). P value and r were calculated by Pearson correlation. F, Pretreatment ctDNA concentration in stage I (n = 7) and stage II–III (n = 30) patients with lung cancer. Data represent mean + SEM. P value was calculated by the Student t test with Welch correction. mo, months; tx, treatment; adeno, adenocarcinoma; squam, squamous cell carcinoma; NOS, not otherwise specified; Sn, sensitivity; Sp, specificity; AUC, area under the curve; PET, positron emission tomography; CT, computed tomography.

Figure 2

Application of ctDNA analysis for posttreatment surveillance in patients with localized lung cancer. A, Both driver and other (i.e., likely passenger) mutations are useful for detection of posttreatment ctDNA. Detection of mutation types pretreatment and at first detectable posttreatment time point is shown. B, Most recurrently mutated driver genes detected pretreatment and at first posttreatment time point. C, Kaplan–Meier analysis for freedom from progression (left) and disease-specific survival (right) stratified by ctDNA detection status during posttreatment surveillance; ever positive (n = 20) versus never positive (n = 17). Landmark analysis was performed from the first posttreatment blood draw. D, Kaplan–Meier analysis of time to ctDNA detection and time to imaging progression from the end of treatment for all patients who experienced posttreatment disease progression by RECIST 1.1 criteria (n = 18); HR = 2.4. P value was calculated by the log-rank test and HR by the Cox exp(beta) method. E, Analysis of ctDNA could aid interpretation of equivocal CT and PET-CT scans during posttreatment surveillance (n = 227 scans from 37 patients). Scans were interpreted as negative, equivocal, or positive by board-certified radiologists and compared with posttreatment ctDNA results and patient recurrence. F, Example of patient with stage IIIB NSCLC with equivocal surveillance imaging and undetectable posttreatment ctDNA who achieves long-term survival. mo, months; tx, treatment; CT, computed tomography; PET, positron emission tomography; squam, squamous cell carcinoma; hGE, haploid genome equivalents; chemoRT, chemoradiotherapy.

Figure 3

Detection of MRD in patients with localized lung cancer. Kaplan–Meier analysis of (A) freedom from progression (left) and disease-specific survival (right) stratified by detection of ctDNA at the MRD landmark (first posttreatment blood draw within 4 months of treatment completion); ctDNA MRD detected (n = 17), not detected (n = 15). P value was calculated by the log-rank test and HR by the Cox exp(beta) method. B, Event chart showing progression by RECIST 1.1 criteria and survival of patients with ctDNA detected at the MRD landmark (red) and patients with no ctDNA detected at the MRD landmark (black). C, Likelihood of detecting ctDNA at the MRD landmark (mean + SEM) by simultaneously tracking all known mutations (n = 65; CAPP-seq), or tracking each mutation separately (n = 65; single reporter). Data represent mean + SEM. P values were calculated by the Student t test. mo, months; tx, treatment.

Figure 4

Analysis of ctDNA for assessment of potential treatment options following ctDNA MRD detection. A, Example of patient with stage IB EGFR-mutant lung adenocarcinoma with detectable ctDNA MRD. B, Mutation load comparison between NSCLC whole-exome sequencing and CAPP-seq. NSCLC mutations from 1,178 tumors determined by whole-exome sequencing by TCGA were intersected with the CAPP-seq lung selector to determine number of mutations that would have been called by CAPP-seq. Linear correlation (Pearson r = 0.93) with equation as shown with ≥5 CAPP-seq nonsynonymous mutations corresponding to >200 whole-exome nonsynonymous mutations. C, Example of patient with stage IIIA NSCLC with detectable ctDNA MRD. D, Analysis of treatment strategies that could potentially have been offered to patients with detectable MRD based on mutation type (i.e., presence of EGFR activating mutation) and mutation load (for selection of patients for immunotherapy). mo, months; tx, treatment; adeno, adenocarcinoma; squam, squamous cell carcinoma; hGE, haploid genome equivalents; SABR, stereotactic ablative radiotherapy; chemoRT, chemoradiotherapy; CR, complete response; TKI, tyrosine kinase inhibitor.