Gliomatosis cerebri (GC) growth pattern: A single-center analysis of clinical, histological, and molecular characteristics of GC and non-GC glioblastoma.

Background: The biological understanding of glioblastoma (GB) with gliomatosis cerebri (GC) pattern is poor due to the absence of GC-specific studies. Here, we aimed to identify molecular or clinical parameters that drive GC growth. Methods: From our methylome database of IDH (isocitrate dehydrogenase)-wildtype GB, we identified 158 non-GC and 65 GC cases. GC cases were subdivided into diffuse-infiltrative (subtype 1), multifocal (subtype 2), or tumors with 1 solid mass (subtype 3). We compared clinical, histological, and molecular parameters and conducted a reference-free tumor deconvolution of DNA methylation data based on latent methylation components (LMC). Results: GC subtype 1 less frequently showed contrast-enhancing tumors, and more frequently lacked morphological GB criteria despite displaying GB DNA methylation profile. However, the tumor deconvolution did not deliver a specific LMC cluster for either of the GC subtypes. Employing the reference-based analysis MethylCIBERSORT, we did not identify significant differences in tumor cell composition. The majority of both GC and non-GC patients received radiochemotherapy as first-line treatment, but there was a major imbalance for resection. The entire GC cohort had significantly shorter overall survival (OS) and time to treatment failure (TTF) than the non-GC cohort. However, when filtering for cases in which only stereotactic biopsy was performed, the comparison of OS and TTF lost statistical significance. Conclusions: Our study offers clinically relevant information by demonstrating a similar outcome for GB with GC growth pattern in the surgically matched analysis. The limited number of cases in the GC subgroups encourages the validation of our DNA methylation analysis in larger cohorts.

DNA methylation subclasses predict the benefit from gross total tumor resection in IDH-wildtype glioblastoma patients.

BACKGROUND: DNA methylation-based tumor classification allows an enhanced distinction into subgroups of glioblastoma. However, the clinical benefit of DNA methylation-based stratification of glioblastomas remains inconclusive. METHODS: Multicentric cohort study including 430 patients with newly diagnosed glioblastoma subjected to global DNA methylation profiling. Outcome measures included overall survival (OS), progression-free survival (PFS), prognostic relevance of EOR and MGMT promoter methylation status as well as a surgical benefit for recurrent glioblastoma. RESULTS: 345 patients (80.2%) fulfilled the inclusion criteria and 305 patients received combined adjuvant therapy. DNA methylation subclasses RTK I, RTK II, and mesenchymal (MES) revealed no significant survival differences (RTK I: Ref.; RTK II: HR 0.9 [95% CI, 0.64-1.28]; p = 0.56; MES: 0.69 [0.47-1.02]; p = 0.06). Patients with RTK I (GTR/near GTR: Ref.; PR: HR 2.87 [95% CI, 1.36-6.08]; p < 0.01) or RTK II (GTR/near GTR: Ref.; PR: HR 5.09 [95% CI, 2.80-9.26]; p < 0.01) tumors who underwent gross-total resection (GTR) or near GTR had a longer OS and PFS than partially resected patients. The MES subclass showed no survival benefit for a maximized EOR (GTR/near GTR: Ref.; PR: HR 1.45 [95% CI, 0.68-3.09]; p = 0.33). Therapy response predictive value of MGMT promoter methylation was evident for RTK I (HR 0.37 [95% CI, 0.19-0.71]; p < 0.01) and RTK II (HR 0.56 [95% CI, 0.34-0.91]; p = 0.02) but not the MES subclass (HR 0.52 [95% CI, 0.27-1.02]; p = 0.06). For local recurrence (n = 112), re-resection conveyed a progression-to-overall survival (POS) benefit (p < 0.01), which was evident in RTK I (p = 0.03) and RTK II (p < 0.01) tumors, but not in MES tumors (p = 0.33). CONCLUSION: We demonstrate a survival benefit from maximized EOR for newly diagnosed and recurrent glioblastomas of the RTK I and RTK II but not the MES subclass. Hence, it needs to be debated whether the MES subclass should be treated with maximal surgical resection, especially when located in eloquent areas and at time of recurrence.

Combination of Immune Checkpoint Inhibitors and Liver-Specific Therapies in Liver-Metastatic Uveal Melanoma: Can We Thus Overcome Its High Resistance?

Uveal Melanoma (UM) is a rare disease; however, it is the most common primary intraocular malignant tumor in adults. Hematogenous metastasis, occurring in up to 50% of cases, mainly to the liver (90%), is associated with poor clinical course and treatment failure. In contrast to dramatic benefits of immunotherapy in many tumor entities, as seen in cutaneous melanoma, immune checkpoint inhibitors (ICI) do not achieve comparable results in Metastatic UM (MUM). The aim of this study was to investigate whether the combination of ICI with liver-directed therapies provides a potential survival benefit for those affected. This retrospective, single-center study, including n = 45 patients with MUM, compared the effect of combining ICI with liver-directed therapy ("Cohort 1") with respect to standard therapies ("Cohort 2") on overall survival (OS). Our results revealed a significant survival difference between Cohort 1 (median OS 22.5 months) and Cohort 2 (median OS 11.4 months), indicating that this combination may enhance the efficacy of immunotherapy and thus provide a survival benefit. There is an urgent need for randomized, prospective trials addressing the combination of liver-directed therapies and various strategies of immunotherapy (such as ICI; IMCgp100; personalized vaccines) in order to establish regimens which finally improve the prognosis of patients with MUM.

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

BACKGROUND: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. METHODS: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). RESULTS: We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. CONCLUSIONS: These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.