Abstract 3401: Whole genome cell-free tumor DNA mutational signatures for noninvasive monitoring of pediatric brain cancers

Abstract

Introduction: Liquid biopsy offers a noninvasive approach to monitor cancer burden during therapy and surveillance period. However, in pediatric brain cancers, liquid biopsy methods from the blood have been unsuccessful due to a low tumor burden and low number of mutations in coding regions. We hypothesized that a whole genome sequencing (WGS)-derived patient specific mutational signature from a matched tumor-normal WGS can provide a sensitive and specific approach to detect mutations in circulating cell free tumor DNA (ctDNA) and provide blood-based monitoring in pediatric patients with brain tumor. Methods: All tumors were analyzed and molecularly subclassified using whole genome DNA methylation profiling and machine learning classifier. Tumor DNA was extracted from pathology tissue and normal germline DNA from the white blood cells, while ctDNA was extracted from 1-2 mL of post-surgery or follow-up plasma samples, WGS was applied to sequence DNA from matched tumor-normal and plasma samples. WGS coverage was 40x for matched tumor-normal DNA and 20x for ctDNA. Using the C2i assay, we derived a personalized mutational pattern for each tumor and used an AI-based error suppression model for quantification and ultra-sensitive detection of ctDNA in plasma samples. A patient-specific personalized genome-wide compendium of somatic mutations was established and ctDNA tested at 1 to 3 available time points during the therapy or surveillance period. An AI-based error suppression model was implemented to filter out the noise in the cell free DNA (cfDNA) while the personalized mutational signature was used to detect the ctDNA in the cfDNA and to amplify the somatic signal contained in it. The ctDNA Tumor Fraction (TF) was compared to the clinical status and MR-based imaging. Results: We profiled 7 pediatric brain tumors, including 2 medulloblastomas (one Group 3, one Group 4), 3 pediatric glioblastomas IDH wild-type, 1 ependymoma PFA subtype and one low grade ganglioglioma. Tumor specific signatures were identified and detected in the plasma of 5 patients with clinical disease with a TF range 0.02-0.0005 but not in 2 patients with no tumor at the time of blood collection. In two children with a medulloblastoma and glioblastoma, the decrease of tumor fraction in ctDNA over 2 (TF: 0.002 to 0.0009) and 3 time points (TF: 0.0005 to undetectable), respectively, correlated with response to therapy based on imaging. Conclusions: Patient-specific WGS tumor signature in ctDNA from blood can be used for sensitive monitoring of children with brain tumors. Citation Format: Ivy Tran, Kristyn Galbraith, Guisheng Zhao, Robyn Borsuk, Joyce Varkey, Sharon Gardner, Jeffrey Allen, David Harter, Jeffrey Wisoff, Eveline T. Hidalgo, Sunil Deochand, Dillon Maloney, Danielle Afterman, Tomer Lauterman, Noah Friedman, Imane Bourzgui, Nidhi Ramaraj, Zohar Donenhirsh, Ronel Veksler, Jonathan Rosenfeld, Ravi Kandasamy, Iman Tavassoly, Boris Oklander, G. Praveen Raju, Theodore Nicolaides, Asaf Zviran, Matija Snuderl. Whole genome cell-free tumor DNA mutational signatures for noninvasive monitoring of pediatric brain cancers [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 3401.

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia

“…We have previously reported that state-transition models can be used to predict time-sequential transcriptome dynamics and disease evolution in biological systems such as murine acute myeloid leukemia (AML) 9,10,18 . Constructing a state-space to model biological transitions can capture changes produced by genetic, epigenetic, or microenvironmental perturbations on the transcriptome, which simultaneously deregulate multiple biological processes in order to generate a phenotype such as the leukemic transformation observed here [26][27][28][29] . We have previously shown that micro-RNA and protein abundance (proteomic) data can also be used to create a state-space and characterize state-transitions 10,30 .…”