Anatomical phenotyping and staging of brain tumours

Prospective trial of the Oncobox platform RNA sequencing bioinformatic analysis for personalized prescription of targeted drugs

Interrogating gene expression in tumor can identify up- and downregulated molecular targets of cancer drugs. Here we report the results of prospective clinical investigation of using RNA sequencing analysis for personalized cancer therapy. Transcriptomic profiles were analyzed using Oncobox platform that identifies altered expression of drug target genes and molecular pathways and builds a personalized rating of targeted therapeutics. Totally, 239 adult solid cancer patients were enrolled: 135 received cancer drug therapy, others received palliative treatment or radiotherapy, or died before therapy started. Oncobox recommended drugs were prescribed in 59 % of the cases receiving therapy. Otherwise, patients received non-targeted therapy or targeted therapy predicted as inefficient by Oncobox (controls). Patients in the Oncobox group were significantly pre-treated compared to controls, but we observed a longer progression-free survival (PFS) trend in the Oncobox group. Furthermore, post-hoc analysis revealed that time between biopsy collection and tumor profiling significantly impacts Oncobox predictive capacity. Excluding patient cases with biopsy obtained more than 7 months before sequencing lead to a significant difference in PFS between Oncobox and control groups with hazard ratio of 0.45 (p-value = 0.023). These results suggest that transcriptomic profiling provides clinically relevant therapeutic match and can improve disease control rate in solid cancers.

Balanced transformer: efficient classification of glioblastoma and primary central nervous system lymphoma

Objective.Primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) are malignant primary brain tumors with different biological characteristics. Great differences exist between the treatment strategies of PCNSL and GBM. Thus, accurately distinguishing between PCNSL and GBM before surgery is very important for guiding neurosurgery. At present, the spinal fluid of patients is commonly extracted to find tumor markers for diagnosis. However, this method not only causes secondary injury to patients, but also easily delays treatment. Although diagnosis using radiology images is non-invasive, the morphological features and texture features of the two in magnetic resonance imaging (MRI) are quite similar, making distinction with human eyes and image diagnosis very difficult. In order to solve the problem of insufficient number of samples and sample imbalance, we used data augmentation and balanced sample sampling methods. Conventional Transformer networks use patch segmentation operations to divide images into small patches, but the lack of communication between patches leads to unbalanced data layers.Approach.To address this problem, we propose a balanced patch embedding approach that extracts high-level semantic information by reducing the feature dimensionality and maintaining the geometric variation invariance of the features. This approach balances the interactions between the information and improves the representativeness of the data. To further address the imbalance problem, the balanced patch partition method is proposed to increase the receptive field by sampling the four corners of the sliding window and introducing a linear encoding component without increasing the computational effort, and designed a new balanced loss function.Main results.Benefiting from the overall balance design, we conducted an experiment using Balanced Transformer and obtained an accuracy of 99.89%, sensitivity of 99.74%, specificity of 99.73% and AUC of 99.19%, which is far higher than the previous results (accuracy of 89.6% ∼ 96.8%, sensitivity of 74.3% ∼ 91.3%, specificity of 88.9% ∼ 96.02% and AUC of 87.8% ∼ 94.9%).Significance.This study can accurately distinguish PCNSL and GBM before surgery. Because GBM is a common type of malignant tumor, the 1% improvement in accuracy has saved many patients and reduced treatment times considerably. Thus, it can provide doctors with a good basis for auxiliary diagnosis.

Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma

Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10 years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate.

The anatomy of neuroepithelial tumours

Many neurological conditions conceal specific anatomical patterns. Their study contributes to the understanding of disease biology and to tailored diagnostics and therapy. Neuroepithelial tumours exhibit distinct anatomical phenotypes and spatiotemporal dynamics that differ from those of other brain tumours. Brain metastases display a preference for the cortico-subcortical boundaries of watershed areas and have a predominantly spherical growth. Primary CNS lymphomas localize to the white matter and generally invade along fibre tracts. In neuroepithelial tumours, topographic probability mapping and unsupervised topological clustering have identified an inherent radial anatomy and adherence to ventriculopial configurations of specific hierarchical orders. Spatiotemporal probability and multivariate survival analyses have identified a temporal and prognostic sequence underlying the anatomical phenotypes of neuroepithelial tumours. Gradual neuroepithelial de-differentiation and declining prognosis follow (i) an expansion into higher order radial units; (ii) a subventricular spread; and (iii) the presence of mesenchymal patterns (expansion along white matter tracts, leptomeningeal or perivascular invasion, CSF spread). While different pathophysiological hypotheses have been proposed, the cellular and molecular mechanisms dictating this anatomical behaviour remain largely unknown. Here we adopt an ontogenetic approach towards the understanding of neuroepithelial tumour anatomy. Contemporary perception of histo- and morphogenetic processes during neurodevelopment permit us to conceptualize the architecture of the brain into hierarchically organized radial units. The anatomical phenotypes in neuroepithelial tumours and their temporal and prognostic sequences share remarkable similarities with the ontogenetic organization of the brain and the anatomical specifications that occur during neurodevelopment. This macroscopic coherence is reinforced by cellular and molecular observations that the initiation of various neuroepithelial tumours, their intratumoural hierarchy and tumour progression are associated with the aberrant reactivation of surprisingly normal ontogenetic programs. Generalizable topological phenotypes could provide the basis for an anatomical refinement of the current classification of neuroepithelial tumours. In addition, we have proposed a staging system for adult-type diffuse gliomas that is based on the prognostically critical steps along the sequence of anatomical tumour progression. Considering the parallels in anatomical behaviour between different neuroepithelial tumours, analogous staging systems may be implemented for other neuroepithelial tumour types and subtypes. Both the anatomical stage of a neuroepithelial tumour and the spatial configuration of its hosting radial unit harbour the potential to stratify treatment decisions at diagnosis and during follow-up. More data on specific neuroepithelial tumour types and subtypes are needed to increase the anatomical granularity in their classification and to determine the clinical impact of stage-adapted and anatomically tailored therapy and surveillance.

Shaping the brain vasculature in development and disease in the single-cell era

The CNS critically relies on the formation and proper function of its vasculature during development, adult homeostasis and disease. Angiogenesis - the formation of new blood vessels - is highly active during brain development, enters almost complete quiescence in the healthy adult brain and is reactivated in vascular-dependent brain pathologies such as brain vascular malformations and brain tumours. Despite major advances in the understanding of the cellular and molecular mechanisms driving angiogenesis in peripheral tissues, developmental signalling pathways orchestrating angiogenic processes in the healthy and the diseased CNS remain incompletely understood. Molecular signalling pathways of the 'neurovascular link' defining common mechanisms of nerve and vessel wiring have emerged as crucial regulators of peripheral vascular growth, but their relevance for angiogenesis in brain development and disease remains largely unexplored. Here we review the current knowledge of general and CNS-specific mechanisms of angiogenesis during brain development and in brain vascular malformations and brain tumours, including how key molecular signalling pathways are reactivated in vascular-dependent diseases. We also discuss how these topics can be studied in the single-cell multi-omics era.

Meta-topologies define distinct anatomical classes of brain tumours linked to histology and survival

The current World Health Organization classification integrates histological and molecular features of brain tumours. The aim of this study was to identify generalizable topological patterns with the potential to add an anatomical dimension to the classification of brain tumours. We applied non-negative matrix factorization as an unsupervised pattern discovery strategy to the fine-grained topographic tumour profiles of 936 patients with neuroepithelial tumours and brain metastases. From the anatomical features alone, this machine learning algorithm enabled the extraction of latent topological tumour patterns, termed meta-topologies. The optimal part-based representation was automatically determined in 10 000 split-half iterations. We further characterized each meta-topology's unique histopathologic profile and survival probability, thus linking important biological and clinical information to the underlying anatomical patterns. In neuroepithelial tumours, six meta-topologies were extracted, each detailing a transpallial pattern with distinct parenchymal and ventricular compositions. We identified one infratentorial, one allopallial, three neopallial (parieto-occipital, frontal, temporal) and one unisegmental meta-topology. Each meta-topology mapped to distinct histopathologic and molecular profiles. The unisegmental meta-topology showed the strongest anatomical-clinical link demonstrating a survival advantage in histologically identical tumours. Brain metastases separated to an infra- and supratentorial meta-topology with anatomical patterns highlighting their affinity to the cortico-subcortical boundary of arterial watershed areas.Using a novel data-driven approach, we identified generalizable topological patterns in both neuroepithelial tumours and brain metastases. Differences in the histopathologic profiles and prognosis of these anatomical tumour classes provide insights into the heterogeneity of tumour biology and might add to personalized clinical decision-making.

GPCR genes as a predictor of glioma severity and clinical outcome

OBJECTIVE

To undertake a comprehensive analysis of the differential expression of the G protein-coupled receptor (GPCR) genes in order to construct a GPCR gene signature for human glioma prognosis.

METHODS

This current study investigated several glioma transcriptomic datasets and identified the GPCR genes potentially associated with glioma severity.

RESULTS

A gene signature comprising 13 GPCR genes (nine upregulated and four downregulated genes in high-grade glioma) was developed. The predictive power of the 13-gene signature was tested in two validation cohorts and a strong positive correlation (Spearman's rank correlation test: ρ = 0.649 for the Validation1 cohort; ρ = 0.693 for the Validation2 cohort) was observed between the glioma grade and 13-gene based severity score in both cohorts. The 13-gene signature was also predictive of glioma prognosis based on Kaplan-Meier survival curve analyses and Cox proportional hazard regression analysis in four cohorts of patients with glioma.

CONCLUSIONS

Knowledge of GPCR gene expression in glioma may help researchers gain a better understanding of the pathogenesis of high-grade glioma. Further studies are needed to validate the association between these GPCR genes and glioma pathogenesis.

Tumor-associated alterations in white matter connectivity have prognostic significance in MGMT-unmethylated glioblastoma

PURPOSE

We investigated the prognostic significance of tumor-associated white matter (TA-WM) tracts in glioblastoma (GBM) using magnetic resonance-diffusion tensor imaging (MR-DTI). We hypothesized that (1) TA-WM tracts harbor microscopic disease not targeted through surgery or radiotherapy (RT), and (2) the greater the extent of TA-WM involvement, the worse the survival outcomes.

METHODS

We studied a retrospective cohort of 76 GBM patients. TA-WM tracts were identified by MR-DTI fractional anisotropy (FA) maps. For each patient, 22 TA-WM tracts were analyzed and each tract was graded 1-3 based on FA. A TA-WM score (TA-WMS) was computed based on number of involved tracts and corresponding FA grade of involvement. Kaplan-Meier statistics were utilized to determine survival outcomes, log-rank test was used to compare survival between groups, and Cox regression was utilized to determine prognostic variables.

RESULTS

For the MGMT-unmethylated cohort, there was a decrease in OS for increasing TA-WMS (median OS 16.5 months for TA-WMS 0-4; 13.6 months for TA-WMS 5-8; 7.3 months for TA-WMS > 9; p = 0.0002). This trend was not observed in the MGMT-methylated cohort. For MGMT-unmethylated patients with TA-WMS > 6 and involvement of tracts passing through brainstem or contralateral hemisphere, median OS was 8.3 months versus median OS 14.1 months with TA-WMS > 6 but not involving aforementioned critical tracts (p = 0.003 log-rank test). For MGMT-unmethylated patients, TA-WMS was predictive of overall survival in multivariate analysis (HR = 1.14, 95% CI 1.03-1.27, p = 0.012) while age, gender, and largest tumor dimension were non-significant.

CONCLUSION

Increased TA-WMS and involvement of critical tracts are associated with decreased overall survival in MGMT-unmethylated GBM.