Abstract B012: M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies
Authors: Tom T. Fischer, Kyle S. Smith, Katie Han, Anna Kostecka, Hong Lin, Daniel Senfter, Tatjana Wedig, Nathalie Schwarz, Natalia Stepien, Sibylle Madlener, Christine Haberler, Esther Hulleman, Sandeep K. Dhanda, Stefan M. Pfister, Santhosh Upadhyaya, Amar Gajjar, Giles W. Robinson, Joonas Haapasalo, Hannu Haapasalo, Kristiina Nordfors, Johannes Gojo, Kendra K. Maass, Kristian W. Pajtler, Paul A. Northcott
Journal: Clinical Cancer Research (2024)

Abstract Background: DNA methylation-based classification has been transformative in the molecular diagnostics of pediatric central nervous system (CNS) tumors. Current diagnostic pipelines integrate histopathology and molecular tools in accordance with the WHO classification of CNS tumors which rely on the availability of tissue specimens. However, some tumors are not amenable to neurosurgical resection due to their high-risk locations and tissue collection is not routinely performed at relapse. In addition, molecular biomarkers are largely lacking for longitudinal disease monitoring in pediatric neuro-oncology. Liquid biopsies (LBs) have emerged as a minimally invasive, longitudinal source of tumor-derived cell-free DNA (cfDNA) ideally suited for detecting minimal residual disease (MRD). In addition, LBs provide a sensitive means of studying tumor evolution at high temporal resolution. Success rates of LB analyses in neuro-oncology have varied drastically between studies, warranting the deployment of more robust and reproducible assays. Methods: We applied a novel cfDNA methylation-based workflow to a large cohort of cerebrospinal fluid (CSF) samples collected from pediatric brain tumor patients (n&amp;gt;200 patients). Enzymatic methylation sequencing (EM-seq) was adapted for CSF-derived cfDNA inputs in the picogram range, enabling the acquisition of genome-wide cfDNA methylation profiles across the cohort. Utilizing non-oncological cfDNA profiles and CNS tumor DNA methylation datasets as a reference, we developed an innovative computational pipeline that incorporates DNA methylation imputation and deep learning of a neural network. In addition, deconvolution of cfDNA profiles facilitated classification of low tumor burden samples, including those with balanced genomes that would have been missed using previous generation assays. Results: Our CSF-based classifier, M-PACT (Methylation-based Predictive Algorithm for CNS Tumors), yielded accurate tumor detection and entity prediction (AUC=0.9) in our benchmarking cohort (n&amp;gt;100 CSF samples). Proof-of-concept studies showed the potential of this methodology to track tumor evolution in serial CSF samples at both the genetic and epigenetic level, illuminating potential mechanisms driving treatment resistance and recurrence. Conclusions: Collectively, this study provides the framework for robust methylation- based tumor detection and classification of CSF LBs. M-PACT can be applied to aid in tumor diagnostics and to detect MRD during follow-up, warranting prospective validation of its broad applicability and clinical utility in future trials. Citation Format: Tom T. Fischer, Kyle S. Smith, Katie Han, Anna Kostecka, Hong Lin, Daniel Senfter, Tatjana Wedig, Nathalie Schwarz, Natalia Stepien, Sibylle Madlener, Christine Haberler, Esther Hulleman, Sandeep K. Dhanda, Stefan M. Pfister, Santhosh Upadhyaya, Amar Gajjar, Giles W. Robinson, Joonas Haapasalo, Hannu Haapasalo, Kristiina Nordfors, Johannes Gojo, Kendra K. Maass, Kristian W. Pajtler, Paul A. Northcott. M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies [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 B012.