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Fragmentomic Analysis of Circulating Tumor DNA Targeted Cancer Panels - Beyond the Abstract

Over the past decade, molecular profiling of tumors has become increasingly important for both clinical decision making and understanding the biological underpinnings of prostate and other cancers. In the past, tumor tissue biopsies have been required for these analyses, but acquiring these specimens is not always feasible due to a multitude of factors. More recently, “liquid biopsies” including the isolation and analysis of circulating tumor cells as well as cell-free DNA (cfDNA) and its tumor-derived subset, circulating tumor DNA (ctDNA), have been utilized for detecting alterations present in the tumor without the need for an invasive biopsy.1,2 Clinical assessment of actionable mutations from blood is commonly performed using targeted cfDNA sequencing panels which can achieve sufficient depth across coding exons to yield robust variant detection.

An emerging area of study is the analysis of cfDNA fragmentation patterns to infer tumor characteristics, referred to as “fragmentomics”. To be detected in the bloodstream, a fragment of cfDNA must be protected from degradation, generally through its association with proteins or protein complexes.3,4 The nucleosome is the most common protein complex that binds and protects cfDNA5,6 which results in enrichment of cfDNA fragments around 167 bp corresponding to DNA wrapped around a single nucleosome.7,8 Distinct cfDNA fragment size distributions have been used to infer nucleosome binding,9,10 gene expression,11 and transcription factor binding.12,13 Fragmentation profiles have further been used to successfully perform cancer detection and cancer subtyping.11,13

However, a major clinical limitation to fragmentomic studies has been the need for whole-genome sequencing (WGS) which is not standard practice in the clinical setting. While some studies perform shallow WGS to alleviate the challenge of cost, these sequencing depths are not sufficient to perform mutation detection which is critical for detecting actionable alterations. Given that multiple targeted exon panels are already used in the clinic, we hypothesized that fragmentation patterns covering coding regions from targeted panels could be utilized for cancer detection and subtyping. To evaluate this we examined cfDNA fragmentation profiles in both a publicly available multi-cancer cfDNA sequencing dataset using the GRAIL cfDNA assay (breast, lung, prostate cancers, non-cancer, N=198), as well as an institutional multi-cancer cohort profiled using a custom cfDNA panel (breast, lung, prostate, bladder cancers, N=320).14 Given that the correlation between fragmentation profiles and gene expression is highest at the transcription start site of a gene, we focused on the fragmentation patterns around the first coding exon of genes in the targeted panel.11,15,16

In our study, we demonstrated that analysis of the fragmentation patterns of first coding exons could identify patients with cancer vs. healthy donors, predict cancer site of origin, and distinguish between prostate cancer subtypes (adenocarcinoma vs neuroendocrine).17 In the UW cohort, training cross validated accuracy to identify cancer type was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. The validation ROC AUCs for all tumor types were ≥0.89 (bladder cancer = 0.98, breast cancer = 0.98, lung cancer = 0.89, prostate cancer = 0.99, NEPC = 1.00. To assess how this approach performs with very low ctDNA fractions, we divided the GRAIL cohort into training and independent validation cohorts based on ctDNA fraction with training performed on high ctDNA fraction samples and validation performed on low ctDNA fraction (<0.05) samples. Training cross validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. The validation ROC AUCs were all ≥0.83 (breast cancer = 0.90, lung cancer = 0.83, prostate cancer = 0.91, tumor vs. normal = 0.99.

Our study demonstrates that fragmentomics of standard targeted ctDNA panels is not only feasible but can accurately identify urologic and non-urologic tumors, as well as prostate cancer subtypes such as neuroendocrine vs. adenocarcinoma, even in samples with low ctDNA content. The use of fragmentation patterns from targeted cfDNA panels would allow for both variant calling and fragmentomics in a single assay which could be leveraged with existing targeted cancer gene panels that are utilized in research and clinical practice. This approach merits consideration for existing and future targeted ctDNA studies considering its implementation can be performed without any additional samples, assays, or sequencing costs.

Written by: Kyle Helzer, Marina Sharifi, Jamie Sperger, Josh Lang, & Shuang (George) Zhao

University of Wisconsin-Madison, Madison, Wisconsin, USA

References:

  1. Zhao, S. G. et al. A clinical-grade liquid biomarker detects neuroendocrine differentiation in prostate cancer. J Clin Invest 132.
  2. Sperger, J. M. et al. Prospective Evaluation of Clinical Outcomes Using a Multiplex Liquid Biopsy Targeting Diverse Resistance Mechanisms in Metastatic Prostate Cancer. J Clin Oncol, JCO2100169.
  3. Yao, W., Mei, C., Nan, X. & Hui, L. Evaluation and comparison of in vitro degradation kinetics of DNA in serum, urine and saliva: A qualitative study. Gene 590, 142-148.
  4. Watanabe, T., Takada, S. & Mizuta, R. Cell-free DNA in blood circulation is generated by DNase1L3 and caspase-activated DNase. Biochem Biophys Res Commun 516, 790-795.
  5. Fan, H. C., Blumenfeld, Y. J., Chitkara, U., Hudgins, L. & Quake, S. R. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci U S A 105, 16266-16271.
  6. Lo, Y. M. et al. Maternal plasma DNA sequencing reveals the genome-wide genetic and mutational profile of the fetus. Sci Transl Med 2, 61ra91.
  7. Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M. & Shendure, J. Cell-free DNA Comprises an In Vivo Nucleosome Footprint that Informs Its Tissues-Of-Origin. Cell 164, 57-68.
  8. Sanchez, C. et al. Circulating nuclear DNA structural features, origins, and complete size profile revealed by fragmentomics. JCI Insight 6.
  9. Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385-389.
  10. Peneder, P. et al. Multimodal analysis of cell-free DNA whole-genome sequencing for pediatric cancers with low mutational burden. Nat Commun 12, 3230.
  11. Esfahani, M. S. et al. Inferring gene expression from cell-free DNA fragmentation profiles. Nat Biotechnol 40, 585-597.
  12. Ulz, P. et al. Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection. Nat Commun 10, 4666.
  13. Doebley, A. L. et al. A framework for clinical cancer subtyping from nucleosome profiling of cell-free DNA. Nat Commun 13, 7475.
  14. Razavi, P. et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat Med 25, 1928-1937.
  15. Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat Genet 48, 1273-1278.
  16. Herberts, C. et al. Deep whole-genome ctDNA chronology of treatment-resistant prostate cancer. Nature 608, 199-208.
  17. Helzer, K. T. et al. Fragmentomic analysis of circulating tumor DNA targeted cancer panels. Ann Oncol.
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