Abstract 3758: Accurate epigenomic estimates of circulating tumor fraction in large-scale clinical data
Authors: Greenwald William W. , He Yupeng , Chen Sai , Jiang Tingting , Valouev Anton , Min Jun , Barbacioru Catalin , Gaile Daniel P. , Ma Dustin , Kim Yvonne , Tran Giao , Wu Indira , Jaimovich Ariel , Raymond Victoria , Nagy Rebecca J. , Chuang Han-Yu 
Published: June 2022

Background: Liquid biopsy offers a rapid and non-invasive alternative to tissue biopsy for identifying biomarkers. More recently, its application has broadened to include assessment of early response to therapy (i.e. molecular response) and in the early-stage settings, detection of minimal residual disease (MRD) and early disease recurrence1. While circulating tumor fraction (cTF) estimated by somatic mutations is well associated with the tumor progression and prognosis, interference can occur from clonal hematopoiesis of indeterminate potential (CHIP), and for cell-free DNA (cfDNA) samples that lack detectable somatic mutations, somatic tumor fraction cannot be estimated. In this analysis, we demonstrate that epigenomic signatures accurately measure cTF using orthogonal analytes to somatic mutations and enable cTF estimation even in cases without detectable tumor driver variants.

Methods: To capture tumor-associated methylated cfDNA, we designed a custom assay of a broad genomic panel (15.2 Mb) targeting unmethylated regions in plasma cfDNA from healthy individuals. We profiled plasma samples from cancer patients with this panel, and utilized machine learning to integrate methylation signals into an estimate of cTF. We benchmarked the accuracy of methylation cTFs on real plasma samples, as well as in-vitro and in-silico titration datasets. Both titration data sets were generated by mixing cfDNA from patients with colorectal cancer (CRC) into the plasma from cancer-free donors, either via titration of CRC cfDNA into cfDNA from cancer-free donors for the in-vitro data, or via computationally mixing reads from CRC patients with those from cancer-free donors for the in-silico data.

Results: Our methylation cTF quantified a similar cTF to those derived from well-calibrated genomic tumor driver mutations; across the 670 stage I-IV CRC samples, a strong correlation (Pearson r=0.85) was observed between methylation logit(cTF) and genomic logit(cTF). The methylation cTF was capable of quantifying low cTFs: it quantified a cTF over 0.1% in &gt;99% of the 270 in-vitro and 1,000 in-silico titration samples with true cTFs &gt;0.1%. In contrast, when applied to 2,037 cancer-free samples, less than 5% of the samples resulted in estimated cTFs of &gt;0.1%. Our methylation cTF was more robust than genomic cTF on the 62 in vitro titration samples with true cTFs between 0.3-1%, with a five fold lower coefficient of variation across methylation cTFs compared to genomic cTFs.

Conclusions: cTFs from methylated cfDNA may overcome the current limitations of somatic mutation based methods. Our methylation approach is capable of accurately detecting cTFs in tumor-driver positive and negative cases. As we estimate tumor-negative cases to be 30-50% of patients with stage I-III cancer and 15-20% of patients with stage IV cancer, our methylation approach may hold promise for providing better evaluation for patient care and management.

Citation Format: William W. Greenwald, Yupeng He, Sai Chen, Tingting Jiang, Anton Valouev, Jun Min, Catalin Barbacioru, Daniel P. Gaile, Dustin Ma, Yvonne Kim, Giao Tran, Indira Wu, Ariel Jaimovich, Victoria Raymond, Rebecca J. Nagy, Han-Yu Chuang. Accurate epigenomic estimates of circulating tumor fraction in large-scale clinical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3758.