Multi-region whole-genome and transcriptomic profiling uncovers plastic, subclone-linked cell states in high-grade diffuse astrocytomas
Ohlsbom, S.; Mäntylä, S.; Nätkin, R.; Hermelo, I.; Nurminen, A.; Tiihonen, A. M.; Salonen, I.; Vuorinen, E.; Nordfors, K.; Haapasalo, H.; Rautajoki, K. J.; Haapasalo, J.; Nykter, M.
Show abstract
Intratumoral heterogeneity is a defining feature of high-grade astrocytomas and a major contributor to treatment resistance. Yet how genomic diversification intersects with transcriptional plasticity remains incompletely understood. We performed high-resolution multi-omic profiling of three complex, treatment-naive tumors (two IDH-wildtype glioblastomas and one IDH-mutant grade 4 astrocytoma). By integrating whole-genome sequencing (WGS), bulk and single-cell RNA sequencing (scRNA-seq), and histopathology across four anatomically distinct regions per tumor, we mapped the co-evolution of genome and transcriptome. Despite striking regional differences in morphology and cellular states, genomic evolution was predominantly trunk-dominated. Most driver alterations were clonal across regions, indicating early acquisition and stable genomic backbones. The IDH-mutant tumor showed linear evolution with localized hypermutation, whereas glioblastomas displayed modest late-branching subclones. In contrast, transcriptional heterogeneity was pronounced and spatially structured. Distinct genetic subclones preferentially occupied divergent transcriptional states. However, subclones shared across regions frequently adopted different phenotypes depending on local microenvironment. Single-cell reconstruction from matched patient-derived cell lines resolved subclone-associated trajectories, revealing dynamic transitions between proliferative and inflammatory states. This study provides a framework for understanding how early-established genomic backbones and regional transcriptional plasticity jointly drive phenotypic diversity. While single biopsies may capture truncal drivers, resolving clinically relevant heterogeneity requires multi-region and single-cell approaches.
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