Abstract IA021: Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution

Abstract Individual bulk tumor biopsies are invasive and may fail to capture clonal heterogeneity within primary tumors and metastatic sites of disease. Further, analyses of subclonal variants are often hindered by background error rates related to tissue preservation methods (e.g. FFPE) and sequencing artifact. Use of plasma-only ultrasensitive liquid biopsy approaches may allow for noninvasive inference of clonality and dynamically track changes in tumor burden. We previously developed MRD-EDGE, a tumor-informed machine learning classifier for ultrasensitive liquid biopsy monitoring of single nucleotide variants (SNVs) and copy number variants (CNVs) using standard plasma whole genome sequencing. For SNVs, MRD- EDGE uses a novel machine learning framework to classify individual cell free DNA (cfDNA) fragments as circulating tumor DNA or cfDNA sequencing artifact. We demonstrated the potential of plasma-only MRD-EDGE to identify SNVs in advanced melanoma and monitor tumor burden throughout treatment with immune checkpoint inhibiton. In tumor-informed settings, MRD-EDGE was used to identify minimal residual disease at tumor fractions (TFs) of 1:100,000 and below. Accurate SNV classification is a function of classifier performance and background error rate. To reduce error rates, we combined MRD-EDGE SNV with paired plus- minus sequencing (ppmSeq) from Ultima Genomics. ppmSeq is a PCR-free approach that leverages the hydrogen bonds of complementary DNA to carry matched native strands directly into sequencing, enabling scalable duplex sequencing. Use of MRD-EDGE SNV together with ppmSeq enables ultrasensitive tracking of early-stage non-small cell lung cancer throughout neoadjuvant immunotherapy. Plasma-only MRD-EDGE is therefore poised to enable dynamic monitoring of tumor burden and clonal composition outside the scope of tumor-informed approaches, including the comparison of SNV composition in cancer recurrence with preoperative disease. Citation Format: Adam Widman, Alexandre P. Cheng, John Zinno, Srinivas Rajagopalan, Ashish Saxena, Nasser Altorki, Dan A. Landau. Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA021.

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Publication Details

Journal
Clinical Cancer Research
Published
2024-11-13
DOI
https://doi.org/10.1158/1557-3265.liqbiop24-ia021
Citations
1
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.29
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article

Abstract IA021: Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution

John Zinno, Dan A. Landau, Adam J. Widman, Nasser K. Altorki et al.
1 citations
Clinical Cancer Research
Cancer Genomics and Diagnostics
0.29
article

Abstract IA021: Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution

John Zinno, Dan A. Landau, Adam J. Widman, Nasser K. Altorki, Ashish Saxena, Srinivas Rajagopalan, Alexandre Pellan Cheng
article en
1 citations

Abstract

Abstract Individual bulk tumor biopsies are invasive and may fail to capture clonal heterogeneity within primary tumors and metastatic sites of disease. Further, analyses of subclonal variants are often hindered by background error rates related to tissue preservation methods (e.g. FFPE) and sequencing artifact. Use of plasma-only ultrasensitive liquid biopsy approaches may allow for noninvasive inference of clonality and dynamically track changes in tumor burden. We previously developed MRD-EDGE, a tumor-informed machine learning classifier for ultrasensitive liquid biopsy monitoring of single nucleotide variants (SNVs) and copy number variants (CNVs) using standard plasma whole genome sequencing. For SNVs, MRD- EDGE uses a novel machine learning framework to classify individual cell free DNA (cfDNA) fragments as circulating tumor DNA or cfDNA sequencing artifact. We demonstrated the potential of plasma-only MRD-EDGE to identify SNVs in advanced melanoma and monitor tumor burden throughout treatment with immune checkpoint inhibiton. In tumor-informed settings, MRD-EDGE was used to identify minimal residual disease at tumor fractions (TFs) of 1:100,000 and below. Accurate SNV classification is a function of classifier performance and background error rate. To reduce error rates, we combined MRD-EDGE SNV with paired plus- minus sequencing (ppmSeq) from Ultima Genomics. ppmSeq is a PCR-free approach that leverages the hydrogen bonds of complementary DNA to carry matched native strands directly into sequencing, enabling scalable duplex sequencing. Use of MRD-EDGE SNV together with ppmSeq enables ultrasensitive tracking of early-stage non-small cell lung cancer throughout neoadjuvant immunotherapy. Plasma-only MRD-EDGE is therefore poised to enable dynamic monitoring of tumor burden and clonal composition outside the scope of tumor-informed approaches, including the comparison of SNV composition in cancer recurrence with preoperative disease. Citation Format: Adam Widman, Alexandre P. Cheng, John Zinno, Srinivas Rajagopalan, Ashish Saxena, Nasser Altorki, Dan A. Landau. Plasma-only ultrasensitive liquid biopsy to monitor tumor burden and clonal evolution [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA021.

Clinical Cancer ResearchVol. 30(21_Supplement)
Memorial Sloan Kettering Cancer Center (US), Cornell University (US), Kettering University (US), New York Genome Center (US), Weill Cornell Medicine (US)
Good health and well-being
Openalex Percentile: Top 38%
Cancer Genomics and Diagnostics
0.29
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