Cell-free DNA from germline TP53 mutation carriers reflect cancer-like fragmentation patterns

Abstract

Germline pathogenic TP53 variants predispose individuals to a high lifetime risk of developing multiple cancers and are the hallmark feature of Li-Fraumeni syndrome (LFS). Our group has previously shown that LFS patients harbor shorter plasma cell-free DNA fragmentation; independent of cancer status. To understand the functional underpinning of cfDNA fragmentation in LFS, we conducted a fragmentomic analysis of 199 cfDNA samples from 82 TP53 mutation carriers and 30 healthy TP53-wildtype controls. We find that LFS individuals exhibit an increased prevalence of A/T nucleotides at fragment ends, dysregulated nucleosome positioning at p53 binding sites, and loci-specific changes in chromatin accessibility at development-associated transcription factor binding sites and at cancer-associated open chromatin regions. Machine learning classification resulted in robust differentiation between TP53 mutant versus wildtype cfDNA samples (AUC-ROC = 0.710-1.000) and intra-patient longitudinal analysis of ctDNA fragmentation signal enabled early cancer detection. These results suggest that cfDNA fragmentation may be a useful diagnostic tool in LFS patients and provides an important baseline for cancer early detection.

Fig. 1. Study and analysis design.

Introduction of the patient and sample cohort profiled (A) and cfDNA fragmentation and analysis metrics utilized (B) in this study. Figure 1/panel A, created with BioRender.com, released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.

Fig. 2. Increased proportion of short cfDNA fragmentation.

A Median fragment size distributions of healthy TP53-wildtype, LFS-H, LFS-PC, and LFS-AC. TP53m-carriers exhibit increased frequency at fragments <150 bp. B Tukey boxplots showing the proportion of fragments within 3 size compartments: 10–150 bp = short, 151–180 bp = mono-nucleosomes, 250–500 bp = di-nucleosomes. TP53m-carriers, independent from cancer status, exhibit increased proportions within the 10–150 bp compartment. C Tukey boxplots showing the proportion of short cfDNA fragments normalized against the global proportion of short cfDNA fragments and proportion of total fragments mapped to each repeat element (normalized contribution). TP53m-carriers exhibit decreased contribution of short cfDNA fragments at these select 10 repeat elements. p-values were calculated using two-sided Student’s t-test compared to TP53-wildtype. D Tukey boxplots showing the proportion of short cfDNA fragments across each germline mutation group by functional TP53 mutation class. Numbers represent the number of samples with the number of patients in brackets. p-values were calculated using Mann–Whitney–Wilcoxon test compared to the median proportion of short cfDNA fragments from all cancer negative TP53m-carriers (red line). * = p-value < 0.05, ** = p-value < 0.01, *** = p-value < 0.001. Exact p-values are provided in Supplementary Data 2. Source data are provided as a Source Data file.

Fig. 3. Altered nucleosome positioning and nucleosome placement.

A Frequency distribution (top), z-scores (middle), and absolute difference (bottom) of the distance of fragment-ends to the closest nucleosome peak in TP53m-carriers. Healthy TP53-wildtype median is displayed as the black curve in each of the top row panels. Increased fragment-ends are observed within the nucleosome trough in all TP53m-carriers. p-values were calculated using a two-sided Kolmogorov-Smirnov test. B Left: Line plot of the median log2(Observed/Expected) frequencies of each AA/AT/TA/TT and CC/CG/GC/GG dinucleotide contexts from nucleosome spanning (167 bp) fragments in TP53m-carriers. The dinucleotide frequencies 50 bp up and downstream of the fragments are also displayed. The median healthy control is displayed as black lines in each of the left panels. Right: Z-scores of dinucleotide frequencies compared to healthy TP53-wildtype controls. TP53-carriers are separated by cancer status. Increased A/T and decreased C/G dinucleotides are observed in TP53m-carriers. Source data are provided as a Source Data file.

Fig. 4. A/T nucleotide bias at cfDNA fragmentation breakpoints.

A Heatmap displaying unsupervised clustering of the frequency of each 256 fragment end-motif. Patient type, cancer history, cancer status, and germline mutation type are displayed above and the GC content of the fragment end-motif is displayed on the right. Distinct clustering between TP53-wildtype and TP53m-carriers is observed. B Dot plot showing the fold change in frequency observed between TP53-wildtype and TP53m-carriers for each of the 256 tetranucleotide end motifs. Gray bars represent the standard deviation, and the GC content of the motif is displayed below. TP53m-carriers display an increased frequency of A/T rich end motifs. C Tukey boxplots of Shannon entropy scores calculated using fragment end-motif frequencies. TP53m-carriers exhibit decreased diversity scores compared to TP53-wildtype samples. D Dot plot showing the median difference between the frequency of each nucleotide up and downstream from fragment cut sites. Error bars represent standard deviation and dot size is representative of p-value. TP53m-carriers display increased frequency of A/T and decreased frequency of C/G nucleotides surrounding fragment cut sites. p-values calculated using two-sided Student’s t-test. * = p-value < 0.05, ** = p-value < 0.01, *** = p-value < 0.001. Exact p-values are provided in Supplementary Data 2. Source data are provided as a Source Data file.

Fig. 5. Differential genome-wide fragmentation.

A Genome-wide fragment ratio profiles (90-150 bp/151-220 bp) of healthy TP53-wildtype controls and TP53m-carriers separated by cancer status. TP53m-carriers show ~1.4x increased variability compared to TP53-wildtype controls. B Heatmap showing unsupervised clustering of genome-wide fragment ratios. Samples were separated by cancer status. Patient type, cancer history, cancer status, and germline mutation type are shown on the right. Pearson’s correlation scores of TP53m-carriers compared to the healthy TP53-wildtype median shown on the right. The healthy TP53-wildtype median fragmentation profile, differentially fragmented regions ( > 3 standard deviations away from healthy TP53-wildtype median), percent of the LFS cohort greater than 3 standard deviations away from the healthy TP53-wildtype median, and the LFS cohort median are displayed on top. C Heatmap showing the Pearson’s correlation scores comparing the median fragment ratio profile between each functional TP53 mutation class, LFS cohort, and healthy TP53-wildtype controls. Each TP53 mutation class shows a low correlation to the healthy control, moderate correlation to other TP53 mutation classes, and highest correlation to the LFS cohort median. D Barcharts showing the proportion of bins with significantly increased or decreased fragment ratios ( > 3 standard deviations away from healthy TP53-wildtype controls) in each functional TP53 mutation class compared to the healthy TP53-wildtype control median. Source data are provided as a Source Data file.

Fig. 6. Altered chromatin accessibility at p53 associated sites.

A Nucleosome positioning tracks (+/−1000bp) for CTCF (top) and p53 (bottom) binding sites split by TP53-wildtype and TP53m-carrier cancer status. Increased variability and decreased central coverage are observed in TP53m-carriers at p53 binding sites. B Nucleosome positioning tracks (+/−1000bp) for the transcription start sites of housekeeping (top) and p53 target (bottom) genes split by TP53-wildtype and TP53m-carrier cancer status. Increased variability and decreased central coverage are observed in TP53m-carriers at the transcription start sites of p53 target genes. Figures show median +/− 1 standard deviation. Source data are provided as a Source Data file.

Fig. 7. Altered chromatin accessibility at development and cancer-associated sites.

A Volcano of transcription factors with differential nucleosome positioning midpoints (left) and amplitudes (right) comparing cancer negative TP53m-carriers to healthy TP53-wildtype controls. p-values were calculated using Student’s two-sided t-test and corrected for multiple comparisons. B Dotplot showing changes in transcription factors with differential midpoint coverage between cancer negative and cancer positive TP53m-carriers. C Nucleosome positioning tracks showing chromatin accessibility at prostate cancer (top) and bladder cancer (bottom) associated open chromatin sites. Samples from patients with active matched cancers are also displayed in each respective plot (blue and yellow). The respective cohort medians are displayed in black +/− 1 standard deviation. D Midpoint coverage and amplitude from nucleosome positioning tracks at cancer-associated open chromatin sites from an array of LFS-associated cancer cohorts obtained from the TCGA. TP53m-carriers exhibit decreased midpoint coverage across cancer types. p-values calculated using two-sided Student’s t-test. * = p-value < 0.05, ** = p-value < 0.01, *** = p-value < 0.001. Exact p-values are provided in Supplementary Data 2. Source data are provided as a Source Data file.

Fig. 8. Fragmentomic classification of TP53 and cancer status.

A ROC curves for each fragmentomic feature. Models were trained using TP53-wildtype controls and cancer-free TP53m-carriers to classify LFS from non-LFS. AUC values with 95% confidence intervals are displayed and color-matched to their respective curve. B Longitudinal integrated fragmentation scores of patients that transitioned clinically from cancer negative to cancer positive or vice versa (phenoconverter). Patient type, cancer status, cancer history, cancer stage, and cancer type are displayed. C Confusion matrix comparing the rates of cancer detection using cell-free cancer fragmentation. Clinical diagnosis was used as the ground truth. Source data are provided as a Source Data file.

Fig. 9. Summary of study findings.

Classification of samples from TP53m-carriers and TP53-wildtype using cfDNA fragmentation features. Using these features and longitudinal sampling, cfDNA fragmentation features can also be used to create personalized cancer monitoring. Portions of Fig. 9 created with BioRender.com, released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.