Bendok, Bernard R.

OBJECTIVE Aneurysm occlusion has been used as surrogate marker of aneurysm treatment efficacy. Aneurysm occlusion scales are used to evaluate the outcome of endovascular aneurysm treatment and to monitor recurrence. These scales, however, require subjective interpretation of imaging data, which can reduce the utility and reliability of these scales and the validity of clinical studies regarding aneurysm occlusion rates. Use of a core lab with independent blinded reviewers has been implemented to enhance the validity of occlusion rate assessments in clinical trials. The degree of agreement between core labs and treating physicians has not been well studied with prospectively collected data. METHODS In this study, the authors analyzed data from the Hydrogel Endovascular Aneurysm Treatment (HEAT) trial to assess the interrater agreement between the treating physician and the blinded core lab. The HEAT trial included 600 patients across 46 sites with intracranial aneurysms treated with coiling. The treating site and the core lab independently reviewed immediate postoperative and follow-up imaging (3–12 and 18–24 months, respectively) using the Raymond-Roy occlusion classification (RROC) scale, Meyer scale, and recanalization survey. A post hoc analysis was performed to calculate interrater reliability using Cohen’s kappa. Further analysis was performed to assess whether degree of agreement varied on the basis of various factors, including scale used, timing of imaging, size of the aneurysm, imaging modality, location of the aneurysm, dome-to-neck ratio, and rupture status. RESULTS Minimal interrater agreement was noted between the core lab reviewers and the treating physicians for assessing aneurysm occlusion using the RROC grading scale (k = 0.39, 95% CI 0.38–0.40) and Meyer scale (k = 0.23, 95% CI 0.14–0.38). The degree of agreement between groups was slightly better but still weak for assessing recanalization (k = 0.45, 95% CI 0.38–0.52). Factors that significantly improved degree of agreement were scales with fewer variables, greater time to follow-up, imaging modality (digital subtraction angiography), and wide-neck aneurysms. CONCLUSIONS Assessment of aneurysm treatment outcome with commonly used aneurysm occlusion scales suffers from risk of poor interrater agreement. This supports the use of independent core labs for validation of outcome data to minimize reporting bias. Use of outcome tools with fewer point categories is likely to provide better interrater reliability. Therefore, the outcome assessment tools are ideal for clinical outcome assessment provided that they are sensitive enough to detect a clinically significant change.

Abstract Characterizing intra- and inter-tumoral heterogeneity of glioblastoma has historically relied on discrete classifications of malignant cell populations leaving immune and other cell populations, known to exist admixed with the malignant tumor cells, relatively neglected. Manifold learning algorithms can manage deconvolving multiple cell populations and are often used to track cell state transitions in single cell transcriptomics. We applied a manifold learning approach to TCGA microarray data (Nf525) and bulk transcriptomics of 134 image localized biopsies across 30 patients with primary and 9 with recurrent glioblastoma to further elucidate how to organize biopsies across a spectrum of possible tissue states. The algorithm revealed a low-dimensional manifold graph for which each biopsy lives across 3 polarizing tissue states - one that is associated with diffusely invaded brain, one that is enriched in mesenchymal genes, and one that is enriched in classical proliferative tumor signatures. We deconvolved the bulk transcriptomics of the image-localized biopsies to reveal the relative abundance of 18 malignant, immune, and other cell subpopulations in each biopsy. Overlaying the cellular decomposition onto the manifold graph visualizing the tissue state distributions revealed that transitions between states correlate with changes in cellular cohabitation composition. The tumor cellular cohabitation ecologies have the lowest diversity, as inferred by ecological measures such as Shannon entropy and evenness, at the distal poles of the graph when compared to the transitional arms. Further, we found that the relationship between imaging appearance of contrast enhancement on T1-weighted MRI and the biopsy cellular composition varies with sex and primary vs recurrent biopsy status. The limited spectrum of possible tissue states revealed by the manifold learning is suggestive of a limited continuum along which tumor and non-tumoral cell populations can cohabitate. Such a limited low-dimensional biological space may constrain the dynamics of tumor biology in a predictable manner.

Abstract Diffuse gliomas are aggressive brain tumors that lack effective therapies. Despite advances in the molecular characterization of gliomas, targeted treatments have failed to improve patient outcomes to date. Here, we employed a functional genomics approach to rapidly identify therapeutic vulnerabilities and provide a strategy to individualize patient treatment. Our process combined comprehensive genomic and transcriptomic tumor profiling, custom algorithms and visualization software for data integration, and preclinical 3-dimensional ex vivo models (mcancers) for drug screening to assess response to therapeutic agents targeting specific molecular alterations. A total of 32 cases, including 5 oligodendrogliomas (1 primary and 4 recurrent), 5 astrocytomas (1 primary and 4 recurrent), and 22 glioblastomas (5 primary and 17 recurrent), were included in the study. Immunohistochemistry and integrated genomics confirmed that mcancers maintain key molecular alterations, including mutations, chromosomal rearrangements and focal amplifications, cellularity, and heterogeneity, faithfully recapitulating the original tumor. A total of 119 single drugs and 183 combinations were tested, including a panel of 26-brain penetrant targeted agents (18 single drugs and 8 combinations) that was curated to target common glioma oncogenic pathways and was tested across all cases. A strong correlation was observed between therapeutic vulnerabilities of glioma mcancers to targeted agents (EGFR, MET, PDGFR, HDAC inhibitors, etc.) and clinical experience. Single agent activity of panel compounds in the ex vivo setting was generally poor, with only 35% mCancers exhibiting > 70% growth inhibition at Cmax. In contrast, combination strategies, especially ones including epigenetic drugs, were significantly more effective (affecting 22 out of 32 mCancers), underscoring the molecular complexity of gliomas and the need for clinical trials testing combination treatments. Our data identified a number of novel combination treatments with significant efficacy across glioma subtypes and molecular profiles, suggesting that our functional genomics pipeline can guide treatment selection and inform clinical trial enrollment for gliomas.