Generation, characterization, and drug sensitivities of 12 patient-derived IDH1-mutant glioma cell cultures

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma

OBJECTIVES

To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion status in isocitrate dehydrogenase (IDH)-mutant astrocytoma.

METHODS

Multiparametric brain MRI data and corresponding genomic information of 234 subjects (111 positives for CDKN2A/B homozygous deletion and 123 negatives for CDKN2A/B homozygous deletion) were obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) respectively. Two independent multi-sequence networks (ResFN-Net and FN-Net) are built on the basis of ResNet and ConvNeXt network combined with attention mechanism to classify CDKN2A/B homozygous deletion status using MR images including contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). The performance of the network is summarized by three-way cross-validation; ROC analysis is also performed.

RESULTS

The average cross-validation accuracy (ACC) of ResFN-Net is 0.813. The average cross-validation area under curve (AUC) of ResFN-Net is 0.8804. The average cross-validation ACC and AUC of FN-Net is 0.9236 and 0.9704, respectively. Comparing all sequence combinations of the two networks (ResFN-Net and FN-Net), the sequence combination of CE-T1WI and T2WI performed the best, and the ACC and AUC were 0.8244, 0.8975 and 0.8971, 0.9574, respectively.

CONCLUSIONS

The FN-Net deep learning networks based on ConvNeXt network achieved promising performance for predicting CDKN2A/B homozygous deletion status of IDH-mutant astrocytoma.

CLINICAL RELEVANCE STATEMENT

A novel deep learning network (FN-Net) based on preoperative MRI was developed to predict the CDKN2A/B homozygous deletion status. This network has the potential to be a practical tool for the noninvasive characterization of CDKN2A/B in glioma to support personalized classification and treatment planning.

KEY POINTS

• CDKN2A/B homozygous deletion status is an important marker for glioma grading and prognosis. • An MRI-based deep learning approach was developed to predict CDKN2A/B homozygous deletion status. • The predictive performance based on ConvNeXt network was better than that of ResNet network.

Genomic Exploration of Distinct Molecular Phenotypes Steering Temozolomide Resistance Development in Patient-Derived Glioblastoma Cells

Chemotherapy using temozolomide is the standard treatment for patients with glioblastoma. Despite treatment, prognosis is still poor largely due to the emergence of temozolomide resistance. This resistance is closely linked to the widely recognized inter- and intra-tumoral heterogeneity in glioblastoma, although the underlying mechanisms are not yet fully understood. To induce temozolomide resistance, we subjected 21 patient-derived glioblastoma cell cultures to Temozolomide treatment for a period of up to 90 days. Prior to treatment, the cells' molecular characteristics were analyzed using bulk RNA sequencing. Additionally, we performed single-cell RNA sequencing on four of the cell cultures to track the evolution of temozolomide resistance. The induced temozolomide resistance was associated with two distinct phenotypic behaviors, classified as "adaptive" (ADA) or "non-adaptive" (N-ADA) to temozolomide. The ADA phenotype displayed neurodevelopmental and metabolic gene signatures, whereas the N-ADA phenotype expressed genes related to cell cycle regulation, DNA repair, and protein synthesis. Single-cell RNA sequencing revealed that in ADA cell cultures, one or more subpopulations emerged as dominant in the resistant samples, whereas N-ADA cell cultures remained relatively stable. The adaptability and heterogeneity of glioblastoma cells play pivotal roles in temozolomide treatment and contribute to the tumor's ability to survive. Depending on the tumor's adaptability potential, subpopulations with acquired resistance mechanisms may arise.

Successes and challenges in modeling heterogeneous BRAFV600E mutated central nervous system neoplasms

Central nervous system (CNS) neoplasms are difficult to treat due to their sensitive location. Over the past two decades, the availability of patient tumor materials facilitated large scale genomic and epigenomic profiling studies, which have resulted in detailed insights into the molecular underpinnings of CNS tumorigenesis. Based on results from these studies, CNS tumors have high molecular and cellular intra-tumoral and inter-tumoral heterogeneity. CNS cancer models have yet to reflect the broad diversity of CNS tumors and patients and the lack of such faithful cancer models represents a major bottleneck to urgently needed innovations in CNS cancer treatment. Pediatric cancer model development is lagging behind adult tumor model development, which is why we focus this review on CNS tumors mutated for BRAFV600E which are more prevalent in the pediatric patient population. BRAFV600E-mutated CNS tumors exhibit high inter-tumoral heterogeneity, encompassing clinically and histopathological diverse tumor types. Moreover, BRAFV600E is the second most common alteration in pediatric low-grade CNS tumors, and low-grade tumors are notoriously difficult to recapitulate in vitro and in vivo. Although the mutation predominates in low-grade CNS tumors, when combined with other mutations, most commonly CDKN2A deletion, BRAFV600E-mutated CNS tumors are prone to develop high-grade features, and therefore BRAFV600E-mutated CNS are a paradigm for tumor progression. Here, we describe existing in vitro and in vivo models of BRAFV600E-mutated CNS tumors, including patient-derived cell lines, patient-derived xenografts, syngeneic models, and genetically engineered mouse models, along with their advantages and shortcomings. We discuss which research gaps each model might be best suited to answer, and identify those areas in model development that need to be strengthened further. We highlight areas of potential research focus that will lead to the heightened predictive capacity of preclinical studies, allow for appropriate validation, and ultimately improve the success of "bench to bedside" translational research.

Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers

BACKGROUND

Patient-derived glioma stem-like cells (GSCs) have become the gold-standard in neuro-oncological research; however, it remains to be established whether loss of in situ microenvironment affects the clinically-predictive value of this model. We implemented a GSC monolayer system to investigate in situ-in vitro molecular correspondence and the relationship between in vitro and patient response to temozolomide (TMZ).

METHODS

DNA/RNA-sequencing was performed on 56 glioblastoma tissues and 19 derived GSC cultures. Sensitivity to TMZ was screened across 66 GSC cultures. Viability readouts were related to clinical parameters of corresponding patients and whole-transcriptome data.

RESULTS

Tumour DNA and RNA sequences revealed strong similarity to corresponding GSCs despite loss of neuronal and immune interactions. In vitro TMZ screening yielded three response categories which significantly correlated with patient survival, therewith providing more specific prediction than the binary MGMT marker. Transcriptome analysis identified 121 genes related to TMZ sensitivity of which 21were validated in external datasets.

CONCLUSION

GSCs retain patient-unique hallmark gene expressions despite loss of their natural environment. Drug screening using GSCs predicted patient response to TMZ more specifically than MGMT status, while transcriptome analysis identified potential biomarkers for this response. GSC drug screening therefore provides a tool to improve drug development and precision medicine for glioblastoma.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Novel kinome profiling technology reveals drug treatment is patient and 2D/3D model dependent in glioblastoma

Glioblastoma is the deadliest brain cancer. One of the main reasons for poor outcome resides in therapy resistance, which adds additional challenges in finding an effective treatment. Small protein kinase inhibitors are molecules that have become widely studied for cancer treatments, including glioblastoma. However, none of these drugs have demonstrated a therapeutic activity or brought more benefit compared to the current standard procedure in clinical trials. Hence, understanding the reasons of the limited efficacy and drug resistance is valuable to develop more effective strategies toward the future. To gain novel insights into the method of action and drug resistance in glioblastoma, we established in parallel two patient-derived glioblastoma 2D and 3D organotypic multicellular spheroids models, and exposed them to a prolonged treatment of three weeks with temozolomide or either the two small protein kinase inhibitors enzastaurin and imatinib. We coupled the phenotypic evidence of cytotoxicity, proliferation, and migration to a novel kinase activity profiling platform (QuantaKinome™) that measured the activities of the intracellular network of kinases affected by the drug treatments. The results revealed a heterogeneous inter-patient phenotypic and molecular response to the different drugs. In general, small differences in kinase activation were observed, suggesting an intrinsic low influence of the drugs to the fundamental cellular processes like proliferation and migration. The pathway analysis indicated that many of the endogenously detected kinases were associated with the ErbB signaling pathway. We showed the intertumoral variability in drug responses, both in terms of efficacy and resistance, indicating the importance of pursuing a more personalized approach. In addition, we observed the influence derived from the application of 2D or 3D models in in vitro studies of kinases involved in the ErbB signaling pathway. We identified in one 3D sample a new resistance mechanism derived from imatinib treatment that results in a more invasive behavior. The present study applied a new approach to detect unique and specific drug effects associated with pathways in in vitro screening of compounds, to foster future drug development strategies for clinical research in glioblastoma.

Drug Repurposing, a Fast-Track Approach to Develop Effective Treatments for Glioblastoma

Glioblastoma (GBM) remains one of the most difficult tumors to treat. The mean overall survival rate of 15 months and the 5-year survival rate of 5% have not significantly changed for almost 2 decades. Despite progress in understanding the pathophysiology of the disease, no new effective treatments to combine with radiation therapy after surgical tumor debulking have become available since the introduction of temozolomide in 1999. One of the main reasons for this is the scarcity of compounds that cross the blood-brain barrier (BBB) and reach the brain tumor tissue in therapeutically effective concentrations. In this review, we focus on the role of the BBB and its importance in developing brain tumor treatments. Moreover, we discuss drug repurposing, a drug discovery approach to identify potential effective candidates with optimal pharmacokinetic profiles for central nervous system (CNS) penetration and that allows rapid implementation in clinical trials. Additionally, we provide an overview of repurposed candidate drug currently being investigated in GBM at the preclinical and clinical levels. Finally, we highlight the importance of phase 0 trials to confirm tumor drug exposure and we discuss emerging drug delivery technologies as an alternative route to maximize therapeutic efficacy of repurposed candidate drug.

Effects of the IDH1 R132H Mutation on the Energy Metabolism: A Comparison between Tissue and Corresponding Primary Glioma Cell Cultures

The R132H mutation in the metabolic enzyme isocitrate dehydrogenase 1 (IDH1) is the most important prognostic factor for the survival of glioma patients. Subsequent studies led to the discovery of a panel of enzymes mainly involved in glutamate anaplerosis and aerobic glycolysis that change in abundance as a result of the IDH1 mutation. To further study these changes, appropriate glioma models are required that accurately mimic in vivo metabolism. To investigate how metabolism is affected by in vitro cell culture, we here compared surgically obtained snap-frozen glioma tissues with their corresponding primary glioma cell culture models with a previously developed targeted mass spectrometry proteomic assay. We determined the relative abundance of a panel of metabolic enzymes. Results confirmed increased glutamate use and decreased aerobic glycolysis in resected IDH1 R132H glioma tissue samples. However, these metabolic profiles were not reflected in the paired glioma primary cell cultures. We suggest that culture conditions and tumor microenvironment play a crucial role in maintaining the in vivo metabolic situation in cell culture models. For this reason, new models that more closely resemble the in vivo microenvironment, such as three-dimensional cell co-cultures or organotypic multicellular spheroid models, need to be developed and investigated.

Metabolic adaptations in cancers expressing isocitrate dehydrogenase mutations

The most frequently mutated metabolic genes in human cancer are those encoding the enzymes isocitrate dehydrogenase 1 (IDH1) and IDH2; these mutations have so far been identified in more than 20 tumor types. Since IDH mutations were first reported in glioma over a decade ago, extensive research has revealed their association with altered cellular processes. Mutations in IDH lead to a change in enzyme function, enabling efficient conversion of 2-oxoglutarate to R-2-hydroxyglutarate (R-2-HG). It is proposed that elevated cellular R-2-HG inhibits enzymes that regulate transcription and metabolism, subsequently affecting nuclear, cytoplasmic, and mitochondrial biochemistry. The significance of these biochemical changes for tumorigenesis and potential for therapeutic exploitation remains unclear. Here we comprehensively review reported direct and indirect metabolic changes linked to IDH mutations and discuss their clinical significance. We also review the metabolic effects of first-generation mutant IDH inhibitors and highlight the potential for combination treatment strategies and new metabolic targets.