Grade-dependent survival and co-mutation landscapes in TP53-mutant gliomas: Biological insights and therapeutic implications

Background: Gliomas display wide heterogeneity across grades, ranging from lower-grade anaplastic tumors (grades 2 and 3) to glioblastoma (GBM, grade 4). TP53 mutations occur frequently, but their prognostic significance depends strongly on co-mutation context and tumor grade. Dissecting these interactions is critical for stratification and therapy. Methods: We analyzed somatic mutation and survival data from The Cancer Genome Atlas (TCGA) and Memorial Sloan Kettering (MSKCC) cohorts from TCGAbiolinks, and cBioPortal. Mutational profiling and co-mutation network analyses were performed using maftools, Kaplan-Meier and Cox regression models evaluated grade- and context-specific survival effects. Therapeutic associations were curated from OncoKB and NCI-MATCH. Results: In grade 2 gliomas, TP53-mutant tumors showed a major survival advantage (40.2 vs 19.4 months in WT), enriched for co-mutations in IDH1 and ATRX. Grade-3 TP53-mutant tumors retained this advantage (32.9 vs 15.2 months) with additional mutations in MUC16 and APOB. By contrast, GBM (grade 4) showed only a modest TP53 benefit (14.8 vs 11.5 months), as protective IDH1/ATRX co-mutations were rare and proliferative drivers (EGFR, PTEN, NF1, PI3K) dominated. Within TP53-mutant gliomas, survival diverged by co-mutation profile. Favorable mutations (ATRX, IDH1, COL6A3, LAMA1, HYDIN, ADGRV1, NAV2) were linked to slower growth, immune visibility, or lower-grade biology. Mutations (PTEN, EGFR, RB1, NF1) exerted modest effects. In contrast, deleterious co-mutations (OBSCN, PRDM9, DNAH11, PTPN3) amplified genomic instability, defining a worst-prognosis subgroup. Conclusions: The prognostic role of TP53 in gliomas is shaped by grade-specific co-mutation context. TP53-mutant tumors with IDH1/ATRX resemble lower-grade gliomas and may benefit from IDH inhibitors, whereas tumors with proliferative or instability drivers may require PI3K/mTOR inhibition or trial enrollment. This integrative framework highlights the need for co-mutation-aware stratification in both prognostication and therapeutic decision-making.