Abstract
Abstract BACKGROUND Plasma circulating tumor DNA (ctDNA) is rarely detectable by traditional methods in patients with GBM. As a result, unlike in lung and other cancers, serial next generation sequencing of ctDNA for monitoring GBM tumor burden has been challenging. In light of the low tumor fraction (TF) of DNA fragments in GBM patient plasma and the urgent need to improve upon MRI for tracking GBM tumor burden, we conducted a pilot study in patients with newly diagnosed GBM using the C2 intelligence platform (C2i Genomics), which leverages genome-wide mutational integration for highly sensitive ctDNA detection. METHODS Plasma was collected pre- and post-operatively in patients with newly diagnosed GBM undergoing surgical resection/biopsy. cfDNA was extracted, quantified, and analyzed for fragment size. Genomic DNA (gDNA) was extracted from matched tumor tissue. Whole genome sequencing (WGS) was performed on both gDNA and cfDNA. A specific copy number alteration (CNA) compendium was created for each patient to generate a readout of TF (Zviran, Nat Medicine 2020). We assessed the association between TF at post-operative day 1 (a surrogate for residual disease) and OS, adjusting for other prognostic factors using Cox regression. RESULTS 37 patients were enrolled. For samples with high tumor fraction (n=5), a statistically significant (p< 1e-4) correlation between CNA profiles of tumor tissue and plasma samples was observed. Post-operative TF above the median value was associated with inferior OS (median 7.7 vs. 19.3 months, p=0.019). This association persisted after adjusting for age, O6-methylguanine-DNA methyltransferase methylation status, extent of resection, and performance status (adjusted HR 2.5, 95% CI 1.1-5.6, p=0.03). CONCLUSION Genome-wide mutational integration enables ultra-sensitive detection of ctDNA in GBM patient plasma. Post-operative TF measured by the C2i test is independently associated with OS in newly diagnosed GBM, providing the foundation to evaluate this technology for personalized prognostication and disease monitoring.
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