Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA

Abstract

The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generation sequencing of circulating tumor DNA (ctDNA) encode tumor-specific radiogenomic expression patterns that can be probed to study this problem. Preliminary findings are reported from a radiogenomic analysis of CT imaging, cfDNA, and ctDNA in 24 patients (median age, 64 years; range, 49-74 years) with stage III lung cancer undergoing chemoradiation on a prospective pilot study (NCT00921739) between September 2009 and September 2014. Unsupervised clustering of radiomic signatures resulted in two clusters that were associated with ctDNA TP53 mutations (P = .03) and changes in cfDNA concentration after 2 weeks of chemoradiation (P = .02). The radiomic features dissimilarity (hazard ratio [HR] = 0.56; P = .05), joint entropy (HR = 0.56; P = .04), sum entropy (HR = 0.53; P = .02), and normalized inverse difference (HR = 1.77; P = .05) were associated with overall survival. These results suggest heterogeneous and low-attenuating disease without a detectable ctDNA TP53 mutation was associated with early surges of cfDNA concentration in response to therapy and a generally better prognosis. Keywords: CT-Quantitative, Radiation Therapy, Lung, Computer Applications-3D, Oncology, Tumor Response, Outcomes Analysis Clinical trial registration no. NCT00921739 Supplemental material is available for this article. © RSNA, 2021.

Keywords: CT-Quantitative; Computer Applications-3D; Lung; Oncology; Outcomes Analysis; Radiation Therapy; Tumor Response.

Conflict of interest statement

Disclosures of Conflicts of Interest: K.J.L. disclosed no relevant relationships. M.N.C. disclosed no relevant relationships. J.G. disclosed no relevant relationships. C.D.J. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by Premera Blue Cross. Other relationships: disclosed no relevant relationships. J.W. disclosed no relevant relationships. Y.C. disclosed no relevant relationships. C.W. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by Duke University Medical Center. Other relationships: disclosed no relevant relationships. A.H. disclosed no relevant relationships. E.X. disclosed no relevant relationships. G.J. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by Inivata; has stock options issued as employee of Inivata. Other relationships: disclosed no relevant relationships. C.R.K. disclosed no relevant relationships. F.F.Y. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: employed by Duke University Hospital; institution receives grant from Varian Medical Systems that is used for other work not related to this study. Other relationships: disclosed no relevant relationships.

Figures

Figure 1:

Pilot study design. (A) Treatment and data acquisition. Prior to chemoradiation, CT images were acquired and phlebotomy was performed to obtain cell-free DNA (cfDNA) from baseline plasma. Patients were treated with a mean dose of 66 Gy with an accelerated fractionation scheme (six fractions/week) with concurrent cisplatin and etoposide. Additional plasma samples were obtained during weeks 2 and 5 of treatment, as well as 6 weeks after treatment. (B) Radiomic transcription and next-generation sequencing. High-throughput radiomic features were extracted from the gross disease within lung parenchyma on CT images, cfDNA concentration was quantified via spectrophotometry, and ctDNA was characterized via next-generation sequencing. (C)Radiogenomic expression panel. For each patient, 61 radiomic features were extracted from the CT image and 36 genes were analyzed for mutational changes. (D) Radiogenomic analysis. Unsupervised data clustering was used to quantify associations between radiomics and genomics, and Cox proportional hazards analysis was used to test the prognostic value of radiomic features.

Figure 2:

Associations between CT radiomics, cell-free DNA (ctDNA) gene expression, and intratreatment changes in cfDNA concentration. (A) The radiomic feature space, Ƒ, according to Equation (1), where column vectors denote tumor-specific radiomic signatures extracted from CT images, and row vectors denote different radiomic features. Two patient clusters were identified based on similarity of radiomic signatures. Comparison to matched eTAm-Seq gene expression data (below) demonstrated a significant association between radiomic signatures and ctDNATP53 (P = .03, Fisher exact test).(B) Patient-specific changes in cfDNA concentration during treatment (baseline, n = 24; 2 weeks,n = 24; 5 weeks, n = 22; after treatment, n = 20), color-coded by radiomic cluster.(C) Clustered radiomic signatures were associated with changes in cfDNA concentration after 2 weeks of chemoradiation (P = .02, t test). The central line denotes the median value; the edges of the box represent the 25th and 75th percentiles; the whiskers are the most extreme data points. eTAm-Seq = enhanced tagged/targeted amplicon sequencing.

Figure 3:

Low-dimensional radiomic feature patterns. The reduced radiomic feature space, Ƒr, following the unsupervised dimensionality reduction and feature selection operation,Ƒ→Ƒr, according to Equation (3). Column vectors of Ƒr denote low-dimensional radiomic signatures, and row vectors ofƑr represent a subset of radiomic features from the complete feature space, Ƒ, that maximize the separation of patients into clusters and minimize the redundancy within those clusters. GLCOM = gray level co-occurrence matrix.