Metabolic Vulnerabilities in Brain Cancer

CRISPRi screen of long non-coding RNAs identifies LINC03045 regulating glioblastoma invasion

INTRODUCTION

Glioblastoma (GBM) invasion studies have focused on coding genes, while few studies evaluate long non-coding RNAs (lncRNAs), transcripts without protein-coding potential, for role in GBM invasion. We leveraged CRISPR-interference (CRISPRi) to evaluate invasive function of GBM-associated lncRNAs in an unbiased functional screen, characterizing and exploring the mechanism of identified candidates.

METHODS

We implemented a CRISPRi lncRNA loss-of-function screen evaluating association of lncRNA knockdown (KD) with invasion capacity in Matrigel. Top screen candidates were validated using CRISPRi and oligonucleotide(ASO)-mediated knockdown in three tumor lines. Clinical relevance of candidates was assessed via The Cancer Genome Atlas(TCGA) and Genotype-Tissue Expression(GTEx) survival analysis. Mediators of lncRNA effect were identified via differential expression analysis following lncRNA KD and assessed for tumor invasion using knockdown and rescue experiments.

RESULTS

Forty-eight lncRNAs were significantly associated with 33-83% decrease in invasion (p<0.01) upon knockdown. The top candidate, LINC03045, identified from effect size and p-value, demonstrated 82.7% decrease in tumor cell invasion upon knockdown, while LINC03045 expression was significantly associated with patient survival and tumor grade(p<0.0001). RNAseq analysis of LINC03045 knockdown revealed that WASF3, previously implicated in tumor invasion studies, was highly correlated with lncRNA expression, while WASF3 KD was associated with significant decrease in invasion. Finally, WASF3 overexpression demonstrated rescue of invasive function lost with LINC03045 KD.

CONCLUSION

CRISPRi screening identified LINC03045, a previously unannotated lncRNA, as critical to GBM invasion. Gene expression is significantly associated with tumor grade and survival. RNA-seq and mechanistic studies suggest that this novel lncRNA may regulate invasion via WASF3.

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

Therapeutic Drug-Induced Metabolic Reprogramming in Glioblastoma

Glioblastoma WHO IV (GBM), the most common primary brain tumor in adults, is a heterogenous malignancy that displays a reprogrammed metabolism with various fuel sources at its disposal. Tumor cells primarily appear to consume glucose to entertain their anabolic and catabolic metabolism. While less effective for energy production, aerobic glycolysis (Warburg effect) is an effective means to drive biosynthesis of critical molecules required for relentless growth and resistance to cell death. Targeting the Warburg effect may be an effective venue for cancer treatment. However, past and recent evidence highlight that this approach may be limited in scope because GBM cells possess metabolic plasticity that allows them to harness other substrates, which include but are not limited to, fatty acids, amino acids, lactate, and acetate. Here, we review recent key findings in the literature that highlight that GBM cells substantially reprogram their metabolism upon therapy. These studies suggest that blocking glycolysis will yield a concomitant reactivation of oxidative energy pathways and most dominantly beta-oxidation of fatty acids.