Sunit Das

Isocitrate dehydrogenase (IDH)-mutant gliomas represent most lower-grade diffuse gliomas in young adults. Although IDH-mutant gliomas initially behave indolently relative to IDH-wildtype counterparts, they undergo inevitable malignant transformation. The mechanisms driving this progression remain poorly defined. We analyzed a unique longitudinal cohort of two patients with IDH-mutant diffuse gliomas, each with matched WHO grade 2, 3, and 4 tumours. Spatial transcriptomics (NanoString GeoMx and 10X Visium), combined with differential expression, spatially variable gene analysis, and gene ontology enrichment, were used to define transcriptional programs underlying progression. Unexpectedly, across both patients, higher-grade tumours demonstrated reduced proliferation at the infiltrative margin as measured by Ki67. Spatial transcriptomic analysis identified an enrichment of invasion-associated genes associated with increasing tumour grade. In an independent bulk RNA-seq cohort, expression of this spatially derived program did not outperform grade-only PFS or OS analysis. These observations underscore the need to develop new metrics that account for spatial evolution that occurs across progression, but escapes detection in bulk analyses and provide the rationale for prioritizing migration-, cytoskeletal- and stress-associated programs for further investigation.

Purpose: Brain metastases (BM) far exceed primary CNS tumours and constitute the majority workload for neuro-oncology care providers. Currently, the cancer registries only capture synchronous BMs, which is only a small proportion of all BMs. We aim to develop and validate a natural language processing (NLP) algorithm that identifies brain metastases in radiology reports, enabling scalable surveillance of asynchronous BMs. Methods: Using population-based cancer registry data in Alberta, Canada, we identified a cancer cohort diagnosed between 2012–2019 with follow-up to 2022. All brain/head radiology reports at and post-cancer diagnosis were identified. Reports were sampled through a multi-phase approach and manually labeled for BM presence. We trained two Bio_ClinicalBERT models on the “Findings” and “Impressions” sections, respectively, and took the maximum predicted probability as the report-level prediction. Internal and external validation used reports from the Canadian provinces of Alberta, Ontario, and British Columbia. Results: The models were trained on 1,879 samples. For internal validation, 1,833 reports from 357 patients were tested. At a probability threshold of 0.4, the model achieved a sensitivity of 0.888 and precision of 0.499. The ensemble substantially outperformed single-section models, which achieved sensitivities of only 67.8% (Findings) and 74.2% (Impressions). On external validation, sensitivity was 0.918 in Ontario and 0.726 in British Columbia, demonstrating robustness across diverse data distributions. Conclusions: An NLP-based pipeline processing both Findings and Impressions sections has been developed and validated in three Canadian provinces. It meets cancer registry operational requirements and to be implemented into the surveillance workflow in Alberta and British Columbia, providing a foundation for population-level BM surveillance.

Background: Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast or lung cancer are at particularly elevated risk of IMD. Early identification of individuals at increased risk could enable targeted surveillance and timely intervention. Methods: We developed interpretable machine-learning competing-risk models to estimate the risk of intracranial metastatic disease among patients with breast or lung cancer. For each cancer type, cause-specific Cox models were combined via the Aalen–Johansen estimator to produce absolute risk estimates at one, three, and five years. Results: Here we show high test set discrimination for intracranial metastatic disease (Uno’s C-index: breast 0.95; lung 0.88) and favorable time-dependent precision–recall performance (AUPRC(t) at 1/3/5 years: breast 0.17/0.53/0.63; lung 0.37/0.61/0.64). Decision-curve analysis across relevant thresholds demonstrates greater net clinical benefit than baseline strategies. Model interpretability analysis identifies cancer stage as the dominant determinant in both cancers; in breast cancer, triple-negative and HER2-positive subtypes contribute additional risk, whereas in lung cancer, histology and tumor size are prominent contributors. Conclusions: Machine-learning based competing-risk survival models offer greater insight into prognostication of intracranial metastatic disease than baseline strategies. These findings support the potential of such models to strengthen personalized risk stratification and guide targeted surveillance for Intracranial metastatic disease among patients with breast or lung cancer.

Background: Whole brain radiation therapy (WBRT) and stereotactic radiation therapy (SRT) are the two most common treatments for patients with intracranial metastatic disease (IMD). In Ontario, Canada, 4 of the 18 (22%) regional cancer centres (RCC) are fitted with dedicated cranial SRT program, which we refer to as SRT-dedicated program centres, while the remaining RCCs deliver SRT but lack a dedicated cranial SRT program. We hypothesized that SRT usage would be higher and patient outcomes better in RCCs with vs. those without an SRT-dedicated program. Methods: Using administrative data housed at the Institute for Clinical Evaluative Sciences (IC/ES), we identified patients who had a primary cancer diagnosis and received cranial RT between April 1, 2010, and August 31, 2023. Treatment centres were grouped based on the presence or absence of an SRT-dedicated program. Within these groupings, hazard ratios (HR) for overall survival (OS) were compared. Additional comparisons based on the movement of patients across centres were performed. Results: The median OS of the cohort was 6.05 months. Significant differences in treatment and patient characteristics were observed according to RT modality used at RCCs with SRT-dedicated programs vs. those without, including the ratio of WBRT vs SRT (OR = 2.8, 95% CI 2.4-3.2, p < 0.0001), patient age ( X ² = 38.2, df = 7, p < 0.0001), stage at diagnosis ( X ² =50.1, df = 4, p < 0.0001), and number of brain metastases treated (OR = 29.9, 95% CI 14.5-76.9, p < 0.0001). The median OS for patients treated with intracranial RT (WBRT or SRT) at centres with vs. those without a dedicated SRT programs was 7.20 vs 5.03 months (HR = 0.81, p < 0.0001), respectively. OS for patients who were treated with SRT at centres with vs. without a dedicated SRT program was 10.15 vs 7.89 months (HR = 0.88, p = 0.0275), respectively. No significant difference in the median OS for patients treated with WBRT at centres with or without a dedicated SRT program was observed (2.96 vs 3.09 months, HR = 1.08, p = 1.0, respectively). Mean OS for patients who travelled to a centre with a dedicated SRT program for intracranial RT after initial systemic therapy at a non-SRT-dedicated centre was associated with an positive impact on OS, compared to OS for patients who received systemic therapy and RT at a non-SRT-dedicated centre (14.0 vs 7.3 months, t = -3.9, p < 0.001). Conclusion: Our study found greater utilization of SRT and significantly longer OS for patients with IMD treated in Ontario RCCs with an SRT-dedicated program. OS was not negatively impacted for patients who travelled to a centre with a dedicated SRT program for radiation therapy.

PURPOSE: CNS tumors are a significant cause of death in the adolescent and young adult (AYA; age 15-39 years) population; however, these patients often lack standardized treatments. METHODS: In Canada, we have established national multidisciplinary virtual AYA CNS tumor board rounds (national rounds) to improve and standardize care. From November 2021 to June 2024, 185 AYA patients with CNS tumors were presented from centers nationwide, including 138 patients with glioma. RESULTS: Before case presentation, 5.1% of patients with glioma were taking targeted agents or were enrolled in clinical trials. However, after national rounds, 72.6% of patients with pediatric-type glioma and 45.9% of patients with adult-type glioma were recommended clinical trials and/or targeted agents. Among the 44 patients with glioma who had received radiation therapy before national rounds, only 14 were recommended further radiation. Cumulatively, 68.9% of patients analyzed received a treatment recommendation that represented a change in clinical management compared with their previous treatments. Concurrently, we performed molecular review of 174 AYA CNS tumors during the study time frame. Using TruSight, we identified gene fusions involving BRAF and FGFR1 in 17.9% and 7.4% of patients with low-grade glioma, respectively. Moreover, single-nucleotide variants in BRAF and FGFR1 were detected in 7.4% and 12.6% of patients with low-grade glioma, respectively. CONCLUSION: Our results suggest that national rounds with centralized molecular review can direct AYA patients with CNS tumors toward targeted agents and clinical trials, while deferring radiation therapy. Taken together, our work details an ongoing effort to improve and standardize care of AYA patients with CNS tumors in Canada.

More than one third of patients with glioblastoma experience tumour progression during adjuvant therapy. In this study, we performed a high-throughput drug repurposing screen of FDA-approved agents capable of crossing the blood-brain barrier that to find agents to counteract acquired or inherent glioma cell resistance to temozolomide-associated cytotoxicity. We identified the cholesterol processing inhibitor, lomitapide, as a potential chemosensitizer in glioblastoma. In vitro treatment of temozolomide-resistant glioblastoma cells with lomitapide resulted in decreased intracellular ubiquinone levels and sensitized cells to temozolomide-induced ferroptosis. Concomitant treatment with lomitapide and temozolomide (TMZ) prolonged survival and delayed tumour recurrence in a mouse glioblastoma model, compared to treatment with TMZ alone. Our data identified lomitapide as a potential adjunct for treatment of temozolomide-resistant glioblastoma.