Patil, Vikas

Abstract Introduction Postoperative radiotherapy is an adjuvant therapy for brain metastases(BrMs) that improves local recurrence(LR). The radiosensitivity index(RSI) is a 10-gene index of pan-cancer molecular radiosensitivity previously validated for predicting radiotherapy response in clinical datasets. In this external validation study, we evaluate RSI as a predictor of local recurrence(LR) after radiotherapy in resected BrM. Methods Patients from University Health Network Toronto(UHN) and McGill University who had a resected BrM and subsequently underwent whole brain radiation(WBRT) or stereotactic radiosurgery(SRS). RSI was calculated using a ranked linear regression model from the weighted expression of 10 radiosensitivity genes, with higher values corresponding to radioresistance. Radiophenotype was determined using a previously validated RSI cutoff of < 0.375 to identify radiosensitive tumors. LR was defined as recurrence in the resection cavity and overall survival(OS) was measured from time of BrM diagnosis. Results 106 BrM patients were included, comprising 29 breast, 37 GI, 33 lung, and 7 melanoma primary sources. Mean age at surgery was 59.2 years, 31.1%(n = 33) were male, 67.9%(n = 72) had WBRT, 23.6%(n = 25) had a subtotal resection, and 30.2%(n = 32) had postoperative chemotherapy. Overall, 34.0%(n = 36) patients had LR. Patients with breast BrMs had median RSI 0.70(IQR:0.55-0.75, 10.3% radiosensitive) with 48.3% LR(n = 14); gastrointestinal BrMs had median RSI 0.43(IQR: 0.35-0.46, 29.7% radiosensitive) and 24.3% LR(n = 9); lung BrMs had median RSI of 0.67(IQR:0.66-0.71, 9% radiosensitive) and 33.3% LR(n = 11); melanoma BrMs had median RSI of 0.72(IQR:0.53-0.92, 0% radiosensitive) and 28.6% LR(n = 2). After adjusting for demographics, primary organ, radiotherapy, resection extent, among others, RSI did not predict LR(p = 0.56) or OS(p = 0.61). Discussion RSI did not predict either LR or OS in patients with surgically resected BrMs status post radiotherapy, further establishing the need for improved predictive scores in this population.

Abstract Purpose: Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor, with a median survival of only 15 months. Effective therapeutic advances remain limited, with few clinical trial patients exhibiting robust responses to experimental therapies. This has led to efforts to identify molecular biomarker signatures capable of stratifying patient responses either prognostically (pre-treatment) or diagnostically (post-treatment). This study aims to assess the robustness and reproducibility of published transcriptional GBM biomarker signatures for predicting clinical outcomes in GBM trials. Methods: We analyzed published RNA-seq data from 4 clinical trials involving oncolytic virotherapy and immune checkpoint inhibitors, as well as multiple cohorts of patients treated via the Stupp protocol. The performance of reported immune gene signatures was evaluated across all datasets using both grouped and continuous survival modeling. Intra-patient signature variation between different biopsy sites and timepoints was assessed using longitudinal, multi-site sampling collected by the Break Through Cancer Accelerating GBM Therapies TeamLab. LASSO, multivariate Cox regression, and risk scoring with FDR adjustment were used to identify novel signatures with cross-cohort utility. Key Findings: No single published signature consistently predicted survival across all analyzed trials. A post-treatment ssGSEA antitumor cytokine signature that was associated with survival in the CAN-3110 OV trial (R = 0.74, p < 0.01) was also significantly associated with improved survival in an adjuvant anti-PD1 trial (R = 0.75, p = 0.03) and neoadjuvant anti-PD1 trial (R = 0.30, p = 0.05), but not in standard-of-care controls. PAM clustering of MCP immune signatures, as reported for the DNX-2401 OV+Anti-PD1 trial, also stratified post-treatment survival in the CAN-3110 OV trial (p < 0.01), with the coldest TME subtype consistently predicting worse survival. However, the prognostic value of the most immune enriched TME subtype was inconsistent and did not extend to the Anti-PD1 trials. Furthermore, multi-site, longitudinal biopsy samples revealed marked heterogeneity in biomarker signatures between biopsies from individual patients, even when assessed at the same timepoint. We’ve identified several candidate transcriptomic signatures that show promise for generalizable prognostic value in GBM patient cohorts and are characterizing their spatiotemporal variability. Conclusions: While no individual immune signature is universally predictive in GBM immunotherapy, certain post-treatment cytokine signatures and TME stratifications are significant in multiple immunotherapy contexts, though not in standard-of-care patient cohorts. Marked spatiotemporal heterogeneity in these signatures underscores the need for composite prognostic markers resilient to sampling variance. Citation Format: Christopher M. Jannotta, C Zoe Linke, Charles A. Whittaker, Vikas Patil, Accelerating GBM Therapies TeamLab, Farshad Nassiri, Gelareh Zadeh, E Antonio Chiocca, Alexander L. Ling. Assessing biomarker reproducibility for glioblastoma patient response stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1055.

Abstract Background DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed. Methods We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade. Results We first demonstrated that all risk groups benefit from gross total resection. All “high-risk, RT sensitive” tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (P = .003); after subtotal resection (STR), from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (P = .004). “Low-risk, RT sensitive cases” (n = 1207, 61.8%) also benefited from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, P = .03), suggesting that RT be considered in these patients. Neither “low-risk RT resistant” (n = 84, 4.3%) nor “high-risk RT resistant” (n = 356, 18.2%) cases benefited from RT, and the latter group was associated with universally poor outcomes. Conclusions We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.