Borad, Mitesh

Background: Tumor mutational burden (TMB) is an FDA-approved biomarker for immune checkpoint inhibitor (ICI) therapy. However, its predictive value varies among tumor types and molecular contexts. We investigated whether a very high TMB identifies a biologically distinct subset and whether a higher cutoff provides additional clinical insights beyond the conventional high TMB threshold. Methods: We analyzed 133 patients with advanced solid tumors and TMB ≥ 10 mutations/Mb (mut/Mb) who underwent tumor genomic profiling using a 523-gene DNA/RNA next-generation sequencing panel. Tumors were stratified into prespecified TMB categories: 10–20 mut/Mb (TMB-H) and >20 mut/Mb (TMB-VH). The clinical characteristics, ICI outcomes (in the treated subset), and pathway-level genomic features were compared between groups. Results: TMB-VH was observed in 42/133 (31.6%) patients and spanned more than 20 tumor types. MSI was markedly more prevalent in TMB-VH than in TMB-H tumors (38.1% vs. 2.2%; Fisher’s exact p = 8.9 × 10−8). Pathway-level comparisons did not identify statistically significant differences after false discovery rate correction (all q > 0.05), and the observed patterns were descriptive in nature. In the ICI-treated subset with complete follow-up, objective response did not differ according to the TMB group. Overall survival (OS) was also similar between groups, whether measured from metastatic diagnosis (log-rank p = 0.937) or from ICI initiation (log-rank p = 0.814), although OS was numerically longer in the TMB-VH group in both analyses without reaching statistical significance. Conclusions: In this cohort study, TMB-VH was strongly associated with MSI but not independently associated with improved ICI outcomes. Larger multicenter cohorts are needed to validate pathway-oriented patterns and clarify the clinical utility of extreme TMB thresholds across various histologies. Integrating the functional context (e.g., MSI status, gene-level context, and pathway-level features) with TMB magnitude may enable more robust, tumor-aware biomarker models for immunotherapy selection.

Abstract High-grade gliomas (HGGs) are aggressive tumors with poor outcomes and limited treatment options. Here, we combined genomic and transcriptomic tumor profiling with drug testing in a patient-derived 3-dimensional culture model to identify individualized treatments and predictive biomarkers. Activity of single agents targeting frequently dysregulated glioma pathways was relatively poor ex vivo and generally reflected historical patient data. However, compounds targeting PI3K, epigenetic, and survival/senescence signaling were effective in some cases. Drug sensitivity correlated with transcriptional rather than genomic features and suggested heterogeneity as a resistance mechanism. Bromodomain and extraterminal domain inhibition was particularly effective in tumors enriched in the mesenchymal transcriptional subtype, promoted proneural transition, and was overcome by upregulated PI3K signaling. Notably, combinations were largely effective, with 6 strategies exhibiting stronger efficacy than corresponding single agents in most cases (58-77%). This study identifies HGG vulnerabilities and associated biomarkers, resistance mechanisms, and effective combination strategies that warrant further clinical validation. Graphical Abstract