Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.publishedVersio

Protocol for derivation of organoids and patient-derived orthotopic xenografts from glioma patient tumors

Tumor organoids and patient-derived orthotopic xenografts (PDOXs) are some of the most valuable pre-clinical tools in cancer research. In this protocol, we describe efficient derivation of organoids and PDOX models from glioma patient tumors. We provide detailed steps for organoid culture, intracranial implantation, and detection of tumors in the brain. We further present technical adjustments for standardized functional assays and drug testing.publishedVersio

Altered metabolic landscape in IDH‐mutant gliomas affects phospholipid, energy, and oxidative stress pathways

Heterozygous mutations in NADP‐dependent isocitrate dehydrogenases (IDH) define the large majority of diffuse gliomas and are associated with hypermethylation of DNA and chromatin. The metabolic dysregulations imposed by these mutations, whether dependent or not on the oncometabolite D‐2‐hydroxyglutarate (D2HG), are less well understood. Here, we applied mass spectrometry imaging on intracranial patient‐derived xenografts of IDH‐mutant versus IDH wild‐type glioma to profile the distribution of metabolites at high anatomical resolution in situ. This approach was complemented by in vivo tracing of labeled nutrients followed by liquid chromatography–mass spectrometry (LC‐MS) analysis. Selected metabolites were verified on clinical specimen. Our data identify remarkable differences in the phospholipid composition of gliomas harboring the IDH1 mutation. Moreover, we show that these tumors are characterized by reduced glucose turnover and a lower energy potential, correlating with their reduced aggressivity. Despite these differences, our data also show that D2HG overproduction does not result in a global aberration of the central carbon metabolism, indicating strong adaptive mechanisms at hand. Intriguingly, D2HG shows no quantitatively important glucose‐derived label in IDH‐mutant tumors, which suggests that the synthesis of this oncometabolite may rely on alternative carbon sources. Despite a reduction in NADPH, glutathione levels are maintained. We found that genes coding for key enzymes in de novo glutathione synthesis are highly expressed in IDH‐mutant gliomas and the expression of cystathionine‐β‐synthase (CBS) correlates with patient survival in the oligodendroglial subtype. This study provides a detailed and clinically relevant insight into the in vivo metabolism of IDH1‐mutant gliomas and points to novel metabolic vulnerabilities in these tumors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Multimodal Imaging of Physiologic Changes Induced by Anti-Angiogenic Therapy in Glioblastoma

Glioblastoma (GBM) is the most frequent and malignant form of primary brain tumors. The standard of care for this disease consists in surgical resection, followed by radiotherapy and chemotherapy. Yet, the infiltrative nature of the disease and the resistance to current therapies cause GBMs to inevitably recur, limiting the prognosis to a little more than a year. GBMs are highly vascularized, and it has long been proposed that interfering with the supply of oxygen and nutrients to the tumor could be used as a therapeutic strategy. Research in this domain has recently lead to the approval in the US of the anti-angiogenic agent bevacizumab for second line treatment of GBMs. An accelerated approval by the Food and Drug Administration was granted on the basis of clinical trials that showed strong radiological response and improved progression free survival in comparison to historical controls. Since then, questions have however been raised about the true antitumor effect of the drug since, despite early improvement in general patient condition, resistance to therapy seems to occur. Benefit in overall survival has not been demonstrated, whether bevacizumab is given as single agent or in combination with chemotherapy. In the present thesis, multimodal imaging techniques were used to assess the changes induced by antiangiogenic therapy in a clinically relevant model of GBM. Radiological findings obtained by Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) were compared to histological and molecular analyses to provide insight into the radiological, physiological, metabolic and molecular responses to the bevacizumab therapy. Using perfusion and diffusion MRI, we observed that anti-angiogenic therapy normalizes the tumor vasculature, and strongly reduces the permeability of blood vessels. While this probably contributes to the improvement in patients condition by reducing peritumoral edema and associated side effects, it also leads to drastic radiological changes which may misleadingly suggest an antitumor effect while the tumor actually continues to grow, possibly adopting a more infiltrative progression pattern. We also found that blood supply to the tumor was strongly reduced after the treatment, an observation of clinical importance that suggests that anti-angiogenic treatment could impair the delivery of systemic chemotherapeutic drugs. The reduced blood supply is also consistent with an increased hypoxia observed in our models when we assessed it by PET. This again has therapeutic significance since increased hypoxia is also associated with reduced efficacy of radiotherapy and chemotherapy. Metabolic changes induced by anti-angiogenic therapy were evaluated in vivo by Magnetic Resonance Spectroscopy and PET analysis of glucose uptake. These studies highlighted an increased glucose consumption and increased glycolytic metabolism in the treated animals, that was later confirmed by metabolomic analysis of tissue extracts. The putative increased acidification of the tumor microenvironment that may result from this glycolytic activity is a factor that favors the infiltration of tumor cells in the brain parenchyma. This suggests that therapies that combine anti-angiogenic compounds with drugs designed to interfere with the impaired metabolic activity of tumors could be interesting as a new treatment. Preliminary results from preclinical studies that use this strategy seem to support this hypothesis. We finally examined the hypothesis that anti-angiogenic therapy could impair neuro-cognitive function, a finding that has recently been suggested in a phase III clinical study. In a small preclinical study using measurements of Long Term Potentiation, a technique classically used in neuroscience to determine memory function, we observed that, in comparison with controls, animals treated with anti-angiogenic therapy showed reduced neuronal plasticity in the hippocampus, a region of the brain associated with spatial learning and short-term memory. These results therefore support the findings of the clinical study but deserve further clinical investigation. In conclusion, the findings in the present thesis show that MRI and PET have complementary roles in the imaging of brain tumors and can be combined to obtain insight into the mechanisms through which tumor cells adapt to anti-angiogenic therapies. Imaging protocols commonly used in the clinic today provide a partial and sometimes misguiding view of the physiological changes induced by the treatment. The addition of new physiologic and cellular imaging techniques could in the future improve our ability to detect, characterize and treat malignant brain tumors, especially in the context of evolving cellular and molecular therapies

Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies

The vastmajority ofmalignant gliomas relapse after surgery and standard radio-chemotherapy. Novelmolecular and cellular therapies are thus being developed, targeting specific aspects of tumor growth.While histopathology remains the gold standard for tumor classification, neuroimaging has over the years taken a central role in the diagnosis and treatment follow up of brain tumors. It is used to detect and localize lesions, define the target area for biopsies, plan surgical and radiation interventions and assess tumor progression and treatment outcome. In recent years the application of novel drugs including anti-angiogenic agents that affect the tumor vasculature, has drastically modulated the outcome of brain tumor imaging. To properly evaluate the effects of emerging experimental therapies and successfully support treatment decisions, neuroimaging will have to evolve. Multimodal imaging systems with existing and new contrast agents, molecular tracers, technological advances and advanced data analysis can all contribute to the establishment of disease relevant biomarkers that will improve diseasemanagement and patient care. In this review,we address the challenges of glioma imaging in the context of novelmolecular and cellular therapies, and take a prospective look at emerging experimental and pre-clinical imaging techniques that bear the promise of meeting these challenges