Sensitive detection of stage I lung adenocarcinoma using plasma cell-free DNA breakpoint motif profiling

Wei Guo, Xin Chen, Rui Liu, Naixin Liang, Qianli Ma, Hua Bao, Xiuxiu Xu, Xue Wu, Shanshan Yang, Yang Shao, Fengwei Tan, Qi Xue, Shugeng Gao, Jie He, Wei Guo, Xin Chen, Rui Liu, Naixin Liang, Qianli Ma, Hua Bao, Xiuxiu Xu, Xue Wu, Shanshan Yang, Yang Shao, Fengwei Tan, Qi Xue, Shugeng Gao, Jie He

Abstract

Background: Early diagnosis benefits lung cancer patients with higher survival, but most patients are diagnosed after metastasis. Although cell-free DNA (cfDNA) analysis holds promise, its sensitivity for detecting early-stage lung cancer is unsatisfying. We leveraged cfDNA fragmentomics to develop a predictive model for invasive stage I lung adenocarcinoma (LUAD).

Methods: 292 stage I LUAD patients from three medical centers were included together with 230 healthy controls whose plasma cfDNA samples were profiled by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic motif features and machine learning models were compared in the training cohort to select the best model. Model performance was assessed in the internal and external validation cohorts and an additional dataset.

Findings: A logistic regression model using the 6bp-breakpoint-motif feature was selected. It yielded 98·0% sensitivity and 94·7% specificity in the internal validation cohort [Area Under the Curve (AUC): 0·985], while 92·5% sensitivity and 90·0% specificity were achieved in the external validation cohort (AUC: 0·954). It is sensitive for early-stage (100% sensitivity for minimally invasive adenocarcinoma, MIA) and <1 cm (92·9%-97·7% sensitivity) tumors. The predictive power remained high when reducing sequencing depth to 0·5× (AUC: 0·977 and 0·931 for internal and external cohorts).

Interpretation: Here we have established a cfDNA breakpoint motif-based model for detecting early-stage LUAD, including MIA and very small-size tumors, shedding light on early cancer diagnosis in clinical practice.

Funding: National Key R&D Program of China; National Natural Science Foundation of China; CAMS Initiative for Innovative Medicine; Special Research Fund for Central Universities, Peking Union Medical College; Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences; Beijing Hope Run Special Fund of Cancer Foundation of China.

Keywords: Cell-free DNA; Early detection; Fragmentomics; Lung cancer; Whole-genome sequencing.

Conflict of interest statement

Declaration of interests XC, RL, HB, XX, XW, SY, and YS are employees of Nanjing Geneseeq Technology Inc., China. All other authors have declared no conflicts of interest.

Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.

Figures

Figure 1
Figure 1
Schematic illustration of the study. (a) Study design. A total of 522 participants (cancer 292, healthy 230) were included in this study. Whole-genome sequencing of plasma cfDNA was performed, and their cfDNA breakpoint motif was profiled. 265 participants (cancer 150, healthy 115) were allocated to training for building the logistic regression algorithm-based machine learning model. 177 participants (cancer 102, healthy 75) were allocated to internal validation for confirming the model performance and determining the cutoff score. 80 participants (cancer 40, healthy 40) were allocated to external validation for evaluating model performance. (b) Schematic diagram of cancer probability score determination. Plasma cfDNA was extracted from the participant's plasma sample and subject to whole-genome sequencing. The sequencing reads that were mapped to a human reference genome were used to determine the 6-nucleotide sequence (i.e., a 6bp breakpoint motif) on each 5′ fragment end (Watson and Crick strands) of plasma cfDNA relative to the genome. The genome-wide breakpoint motif profile was then applied in the logistic regression algorithm to calculate the participant's cancer probability score.
Figure 2
Figure 2
Identification and evaluation of the 6bp breakpoint motif logistic regression model for cancer prediction. (a) ROC curve evaluating the performance of predictive models built on different cfDNA motif features and machine learning algorithms in distinguishing early lung cancer from healthy subjects for the combined validation cohorts (DL: deep learning; XG: XGBoost; RL: logistic regression); (b) the sensitivities and specificities of different predictive models from (a) in the combined validation cohorts; (c) Heat map analyzing frequencies of the 65 breakpoint motifs in the training model with non-zero coefficients between healthy and cancer subjects in the validation cohorts. The data are row-normalized; (d) Box plot showing frequencies between healthy and cancer subjects in the validation cohorts for the four representative motifs contributing most significantly to the model (*: 0·01 < p < 0·05; **: 0·001 < p < 0·01; ***: p < 0·001, Wilcoxon rank-sum test).
Figure 3
Figure 3
Development and evaluation of the predictive model in internal and external validation cohorts. (a) ROC curve evaluating the overall performance of the predictive model all using 5× coverage WGS data in distinguishing early lung cancer from healthy subjects for the internal and external validation cohorts; (b) The boxplot showing the distribution of cancer scores based on the 5× WGS model in the patient and control groups of the validation cohorts. The 95% specificity cutoff score for the internal validation set is 0·3725 (*: 0·01 < p < 0·05; **: 0·001 < p < 0·01; ***: p < 0·001, t-test); (c) and (d) ROC curves evaluating the 5× WGS-based model performance using low-coverage (4×-0·5×) WGS data in internal and external validation cohorts.
Figure 4
Figure 4
The model's diagnostic sensitivities in different subgroups of the combined validation cohorts at 95% specificity. The sensitivities (%) were calculated with 95% confidence interval as indicated by the bars for subgroups of (a) histology, (b) stage, (c) differentiation level, (d) tumor size, (e) age, (f) sex, (g) smoking, (h) drinking, (i) predominant histologic pattern, (j) tumor location and (k) focality. The numbers in the parentheses represent the true positive and total cases in each subgroup category.

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