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Fulltext Remote assessment of cognition and quality of life following radiotherapy for glioma: deep-learning-based predictive models and MRI correlates

Voon NS, Manan HA, Yahya N

J Neurooncol, 2023 Apr 04.
PMID: 37014593 DOI: 10.1007/s11060-023-04303-9

BACKGROUND: Glioma irradiation often unavoidably damages the brain volume and affects cognition. This study aims to evaluate the relationship of remote cognitive assessments in determining cognitive impairment of irradiated glioma patients in relation to the quality of life and MRI changes.
METHODS: Thirty patients (16-76 aged) with two imaging (pre- and post-RT) and completed cognitive assessments were recruited. Cerebellum, right and left temporal lobes, corpus callosum, amygdala and spinal cord were delineated and their dosimetry parameters were collected. Cognitive assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE)). Regression models and deep neural network (DNN) were used to evaluate the relationship between brain volume, cognition and treatment dose in patients.
RESULTS: Cognitive assessments were highly inter-correlated (r > 0.9) and impairment was shown between pre- and post-RT findings. Brain volume atrophy was shown post-RT, and cognitive impairments were correlated with radiotherapy-associated volume atrophy and dose-dependent in the left temporal lobe, corpus callosum, cerebellum and amygdala. DNN showed a good area under the curve for cognitive prediction; TICS (0.952), T-MoCA (0.909) and Tele-MACE (0.822).
CONCLUSIONS: Cognition can be evaluated remotely in which radiotherapy-related brain injury is dose-dependent and volume-dependent. Prediction models can assist in the early identification of patients at risk for neurocognitive decline following RT for glioma, thus facilitating potential treatment interventions.
Fulltext A multi-resolution textural approach to diagnostic neuropathology reporting

Fauzi MF, Gokozan HN, Elder B, Puduvalli VK, Pierson CR, Otero JJ, et al.

J Neurooncol, 2015 Sep;124(3):393-402.
PMID: 26255070 DOI: 10.1007/s11060-015-1872-4

We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.
ATM inhibition prevents interleukin-6 from contributing to the proliferation of glioblastoma cells after ionizing radiation

Lim YC, Quek H, Offenhäuser C, Fazry S, Boyd A, Lavin M, et al.

J Neurooncol, 2018 Jul;138(3):509-518.
PMID: 29564746 DOI: 10.1007/s11060-018-2838-0

Glioblastoma (GBM) is a highly fatal disease with a 5 year survival rate of less than 22%. One of the most effective treatment regimens to date is the use of radiotherapy which induces lethal DNA double-strand breaks to prevent tumour growth. However, recurrence occurs in the majority of patients and is in-part a result of robust radioresistance mechanisms. In this study, we demonstrate that the multifunctional cytokine, interleukin-6 (IL-6), confers a growth advantage in GBM cells but does not have the same effect on normal neural progenitor cells. Further analysis showed IL-6 can promote radioresistance in GBM cells when exposed to ionising radiation. Ablation of the Ataxia-telangiectasia mutated serine/threonine kinase that is recruited and activated by DNA double-strand breaks reverses the effect of radioresistance and re-sensitised GBM to DNA damage thus leading to increase cell death. Our finding suggests targeting the signaling cascade of DNA damage response is a potential therapeutic approach to circumvent IL-6 from promoting radioresistance in GBM.
Fulltext Virus vector-mediated genetic modification of brain tumor stromal cells after intravenous delivery

Volak A, LeRoy SG, Natasan JS, Park DJ, Cheah PS, Maus A, et al.

J Neurooncol, 2018 Sep;139(2):293-305.
PMID: 29767307 DOI: 10.1007/s11060-018-2889-2

The malignant primary brain tumor, glioblastoma (GBM) is generally incurable. New approaches are desperately needed. Adeno-associated virus (AAV) vector-mediated delivery of anti-tumor transgenes is a promising strategy, however direct injection leads to focal transgene spread in tumor and rapid tumor division dilutes out the extra-chromosomal AAV genome, limiting duration of transgene expression. Intravenous (IV) injection gives widespread distribution of AAV in normal brain, however poor transgene expression in tumor, and high expression in non-target cells which may lead to ineffective therapy and high toxicity, respectively. Delivery of transgenes encoding secreted, anti-tumor proteins to tumor stromal cells may provide a more stable and localized reservoir of therapy as they are more differentiated than fast-dividing tumor cells. Reactive astrocytes and tumor-associated macrophage/microglia (TAMs) are stromal cells that comprise a large portion of the tumor mass and are associated with tumorigenesis. In mouse models of GBM, we used IV delivery of exosome-associated AAV vectors driving green fluorescent protein expression by specific promoters (NF-κB-responsive promoter and a truncated glial fibrillary acidic protein promoter), to obtain targeted transduction of TAMs and reactive astrocytes, respectively, while avoiding transgene expression in the periphery. We used our approach to express the potent, yet toxic anti-tumor cytokine, interferon beta, in tumor stroma of a mouse model of GBM, and achieved a modest, yet significant enhancement in survival compared to controls. Noninvasive genetic modification of tumor microenvironment represents a promising approach for therapy against cancers. Additionally, the vectors described here may facilitate basic research in the study of tumor stromal cells in situ.
Transcription factors NFIA and NFIB induce cellular differentiation in high-grade astrocytoma

Chen KS, Bridges CR, Lynton Z, Lim JWC, Stringer BW, Rajagopal R, et al.

J Neurooncol, 2020 Jan;146(1):41-53.
PMID: 31760595 DOI: 10.1007/s11060-019-03352-3

INTRODUCTION: Malignant astrocytomas are composed of heterogeneous cell populations. Compared to grade IV glioblastoma, low-grade astrocytomas have more differentiated cells and are associated with a better prognosis. Therefore, inducing cellular differentiation to alter the behaviour of high-grade astrocytomas may serve as a therapeutic strategy. The nuclear factor one (NFI) transcription factors are essential for normal astrocytic differentiation. Here, we investigate whether family members NFIA and NFIB act as effectors of cellular differentiation in glioblastoma.
METHODS: We analysed expression of NFIA and NFIB in mRNA expression data of high-grade astrocytoma and with immunofluorescence co-staining. Furthermore, we induced NFI expression in patient-derived subcutaneous glioblastoma xenografts via in vivo electroporation.
RESULTS: The expression of NFIA and NFIB is reduced in glioblastoma as compared to lower grade astrocytomas. At a cellular level, their expression is associated with differentiated and mature astrocyte-like tumour cells. In vivo analyses consistently demonstrate that expression of either NFIA or NFIB is sufficient to promote tumour cell differentiation in glioblastoma xenografts.
CONCLUSION: Our findings indicate that both NFIA and NFIB may have an endogenous pro-differentiative function in astrocytomas, similar to their role in normal astrocyte differentiation. Overall, our study establishes a basis for further investigation of targeting NFI-mediated differentiation as a potential differentiation therapy.