AKT pathway genes define 5 prognostic subgroups in glioblastoma

Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis

PURPOSE

Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients.

METHODS

We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality.

RESULTS

A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (βˆ = 0.66; P = .048) on survival (βˆ = 0.37; P = .008).

CONCLUSION

Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.

Differential gene expression-based connectivity mapping identified novel drug candidate and improved Temozolomide efficacy for Glioblastoma

BACKGROUND

Glioblastoma (GBM) has a devastating median survival of only one year. Treatment includes resection, radiation therapy, and temozolomide (TMZ); however, the latter increased median survival by only 2.5 months in the pivotal study. A desperate need remains to find an effective treatment.

METHODS

We used the Connectivity Map (CMap) bioinformatic tool to identify candidates for repurposing based on GBM's specific genetic profile. CMap identified histone deacetylase (HDAC) inhibitors as top candidates. In addition, Gene Expression Profiling Interactive Analysis (GEPIA) identified HDAC1 and HDAC2 as the most upregulated and HDAC11 as the most downregulated HDACs. We selected PCI-24781/abexinostat due to its specificity against HDAC1 and HDAC2, but not HDAC11, and blood-brain barrier permeability.

RESULTS

We tested PCI-24781 using in vitro human and mouse GBM syngeneic cell lines, an in vivo murine orthograft, and a genetically engineered mouse model for GBM (PEPG - PTENflox/+; EGFRvIII+; p16Flox/- & GFAP Cre +). PCI-24781 significantly inhibited tumor growth and downregulated DNA repair machinery (BRCA1, CHK1, RAD51, and O6-methylguanine-DNA- methyltransferase (MGMT)), increasing DNA double-strand breaks and causing apoptosis in the GBM cell lines, including an MGMT expressing cell line in vitro. Further, PCI-24781 decreased tumor burden in a PEPG GBM mouse model. Notably, TMZ + PCI increased survival in orthotopic murine models compared to TMZ + vorinostat, a pan-HDAC inhibitor that proved unsuccessful in clinical trials.

CONCLUSION

PCI-24781 is a novel GBM-signature specific HDAC inhibitor that works synergistically with TMZ to enhance TMZ efficacy and improve GBM survival. These promising MGMT-agnostic results warrant clinical evaluation.

Individualizing the definition of seizure clusters based on temporal clustering analysis

OBJECTIVE

Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 h. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person's seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a principled statistical approach to defining seizure clusters that addresses these issues.

METHODS

A total of 533,968 clinical seizures from 1,748 people with epilepsy in the Seizure Tracker™ seizure diary database were used for algorithm development. We propose an algorithm for automated individualized seizure cluster identification combining cumulative sum change-point analysis with bootstrapping and aberration detection, which provides a new approach to personalized seizure cluster identification at user-specified levels of clinical significance. We develop a standalone user interface to make the proposed algorithm accessible for real-time seizure cluster identification (ClusterCalc™). Clinical impact of systematizing cluster identification is demonstrated by comparing empirically-defined clusters to those identified by routine seizure cluster definitions. We also demonstrate use of the Hurst exponent as a standardized measure of seizure clustering for comparison of seizure clustering burden within or across patients.

RESULTS

Seizure clustering was present in 26.7 % (95 % CI, 24.5-28.7 %) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. Using the proposed algorithm, we found that 37.7-59.4 % of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified by the algorithm were missed by conventional definitions. The utility of the ClusterCalc algorithm for individualized seizure cluster detection is demonstrated.

SIGNIFICANCE

This study proposes a principled statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters.

BCL3 expression promotes resistance to alkylating chemotherapy in gliomas

The response of patients with gliomas to alkylating chemotherapy is heterogeneous. However, there are currently no universally accepted predictors of patient response to these agents. We identify the nuclear factor κB (NF-κB) co-regulator B cell CLL/lymphoma 3 (BCL-3) as an independent predictor of response to temozolomide (TMZ) treatment. In glioma patients with tumors that have a methylated O⁶-methylguanine DNA methyltransferase (MGMT) promoter, high BCL-3 expression was associated with a poor response to TMZ. Mechanistically, BCL-3 promoted a more malignant phenotype by inducing an epithelial-to-mesenchymal transition in glioblastomas through promoter-specific NF-κB dimer exchange. Carbonic anhydrase II (CAII) was identified as a downstream factor promoting BCL-3-mediated resistance to chemotherapy. Experiments in glioma xenograft mouse models demonstrated that the CAII inhibitor acetazolamide enhanced survival of TMZ-treated animals. Our data suggest that BCL-3 might be a useful indicator of glioma response to alkylating chemotherapy and that acetazolamide might be repurposed as a chemosensitizer for treating TMZ-resistant gliomas.

Lumbar puncture-administered resveratrol inhibits STAT3 activation, enhancing autophagy and apoptosis in orthotopic rat glioblastomas

Trans-resveratrol suppresses glioblastoma growth in vitro, but its effects on intracranial glioblastomas remain untested. Resveratrol crosses the blood–brain barrier, and lumbar puncture (LP) greatly increases its bioavailability in rat brains; therefore, we investigated the effectiveness of LP-administered resveratrol on orthotopic rat glioblastomas. Twenty-four tumor-bearing rats were separated into two groups: Group 1 receiving 100 μl saline containing 0.3% DMSO and Group 2 receiving 100 μl resveratrol (300 μM). Treatments started 3 days after transplantation in 2-day intervals until death. Intracranial drug availabilities, tumor sizes, average life spans and the impacts on STAT3 signaling, apoptosis and autophagy rates were evaluated. MRI imaging revealed that average tumor size in the LP group (495.8 ± 22.3 mm²) was smaller than the control groups (810.3 ± 56.4 mm²; P<0.05). The mean survival time in the LP group (22.2 ± 2.1 d) was longer than control animals (16.0 ± 1.8 d; P<0.05). LP resveratrol-treated glioblastomas showed less Cyclin D1 staining, enhanced autophagy with up-regulated LC3 and Beclin1 expression, and widely distributed apoptotic foci around tumor capillaries with suppressed STAT3 expression and nuclear translocation. In conclusion, LP-delivered resveratrol efficiently inhibited orthotopic rat glioblastoma growth by inactivating STAT3 signaling and enhancing autophagy and apoptosis.

Survival kinase genes present prognostic significance in glioblastoma

Cancer biomarkers with a strong predictive power for diagnosis/prognosis and a potential to be therapeutic targets have not yet been fully established. Here we employed a loss-of-function screen in glioblastoma (GBM), an infiltrative brain tumor with a dismal prognosis, and identified 20 survival kinase genes (SKGs). Survival analyses using The Cancer Genome Atlas (TCGA) datasets revealed that the expression of CDCP1, CDKL5, CSNK1E, IRAK3, LATS2, PRKAA1, STK3, TBRG4, and ULK4 stratified GBM prognosis with or without temozolomide (TMZ) treatment as a covariate. For the first time, we found that GBM patients with a high level of NEK9 and PIK3CB had a greater chance of having recurrent tumors. The expression of CDCP1, IGF2R, IRAK3, LATS2, PIK3CB, ULK4, or VRK1 in primary GBM tumors was associated with recurrence-related prognosis. Notably, the level of PIK3CB in recurrent tumors was much higher than that in newly diagnosed ones. Congruent with these results, genes in the PI3K/AKT pathway showed a significantly strong correlation with recurrence rate, further highlighting the pivotal role of PIK3CB in the disease progression. Importantly, 17 SKGs together presented a novel GBM prognostic signature. SKGs identified herein are associated with recurrence rate and present prognostic significance in GBM, thereby becoming attractive therapeutic targets.

Diagnostic and Therapeutic Biomarkers in Glioblastoma: Current Status and Future Perspectives

Glioblastoma (GBM) is a primary neuroepithelial tumor of the central nervous system, characterized by an extremely aggressive clinical phenotype. Patients with GBM have a poor prognosis and only 3–5% of them survive for more than 5 years. The current GBM treatment standards include maximal resection followed by radiotherapy with concomitant and adjuvant therapies. Despite these aggressive therapeutic regimens, the majority of patients suffer recurrence due to molecular heterogeneity of GBM. Consequently, a number of potential diagnostic, prognostic, and predictive biomarkers have been investigated. Some of them, such as IDH mutations, 1p19q deletion, MGMT promoter methylation, and EGFRvIII amplification are frequently tested in routine clinical practice. With the development of sequencing technology, detailed characterization of GBM molecular signatures has facilitated a more personalized therapeutic approach and contributed to the development of a new generation of anti-GBM therapies such as molecular inhibitors targeting growth factor receptors, vaccines, antibody-based drug conjugates, and more recently inhibitors blocking the immune checkpoints. In this article, we review the exciting progress towards elucidating the potential of current and novel GBM biomarkers and discuss their implications for clinical practice.

Increased infiltration and tolerised antigen-specific CD8+ TEM cells in tumor but not peripheral blood have no impact on survival of HCMV+ glioblastoma patients

Human cytomegalovirus (HCMV) antigens in glioblastoma (GBM) present opportunities for personalised immunotherapy. However, their presence in GBM tissue is still under debate, and evidence of their impact on functional immune responses and prognosis is sparse. Here, we investigated the presence of pp65 (UL83) and immediate early 1 (IE-1) HCMV antigens in a cohort of Norwegian GBM patients (n = 177), using qPCR, immunohistochemistry, and serology. HCMV status was then used to investigate whether viral antigens influenced immune cell phenotype, infiltration, activation and patient survival. Pp65 and IE-1 were detected by qPCR in 23% and 43% of GBM patients, respectively. Furthermore, there was increased seropositivity in GBM patients relative to donors (79% vs. 48%, respectively; Logistic regression, OR = 4.05, 95%CI [1.807-9.114], P = 0.001, also when adjusted for age (OR = 2.84, 95%CI [1.110-7.275], P = 0.029). Tissue IE-1-positivity correlated with increased CD3⁺CD8⁺ T-cell infiltration (P < 0.0001), where CD8⁺ effector memory T (T_EM) cells accounted for the majority of CD8⁺T cells compared with peripheral blood of HCMV⁺ patients (P < 0.0001), and HCMV⁺ (P < 0.001) and HCMV^- (P < 0.001) donors. HLA-A2/B8-restricted HCMV-specific CD8⁺ T cells were more frequent in blood and tumor of HCMV⁺ GBM patients compared with seronegative patients, and donors irrespective of their serostatus. In biopsies, the HCMV-specific CD8⁺ T_EM cells highly expressed CTLA-4 and PD-1 immune checkpoint protein markers compared with populations in peripheral blood (P < 0.001 and P < 0.0001), which expressed 3-fold greater levels of CD28 (P < 0.001 and P < 0.0001). These peripheral blood T cells correspondingly secreted higher levels of IFNγ in response to pp65 and IE-1 peptide stimulation (P < 0.001). Thus, despite apparent increased immunogenicity of HCMV compared with tumor antigens, the T cells were tolerised, and HCMV status did not impact patient survival (Log Rank_3.53 HR = 0.85 95%CI [0.564-1.290], P = 0.45). Enhancing immune functionality in the tumor microenvironment thus may improve patient outcome.

The role of AKT isoforms in glioblastoma: AKT3 delays tumor progression

The growth factor receptor/PI3K/AKT pathway is an important drug target in many cancers including Glioblastoma. AKT, a key node in the pathway, has 3 isoforms, AKT1, AKT2 and AKT3. Here we investigate their role in GBM. We find each activated, ser473 phosphorylated isoform is present in some GBMs but expression patterns vary. There is a direct relationship between human GBM patient outcome and both AKT1 and AKT2 mRNA levels, but an inverse relationship with AKT3 mRNA. Furthermore, AKT3 mRNA levels were high in a less aggressive GBM subtype. Overexpressing AKT3 improves survival in a rodent model of GBM and decreases colony forming efficiency, but not growth rate, in glioma cells. Silencing AKT3 slows cell cycle progression in one cell line and increases apoptosis in another. Our studies of AKT3 substrates indicate (1) silencing both AKT2 and AKT3 reduces GSK3 phosphorylation (2) only AKT2 silencing reduces S6 phosphorylation. Since S6 phosphorylation is a marker of mTORC1 activity this indicates that AKT2 activates mTORC1, but AKT3 does not. Our results indicate AKT isoforms have different roles and downstream substrates in GBM. Unexpectedly, they indicate AKT3 delays tumor progression. Therefore strategies that inhibit AKT3 may be unhelpful in some GBM patients.

CUSP9* treatment protocol for recurrent glioblastoma: aprepitant, artesunate, auranofin, captopril, celecoxib, disulfiram, itraconazole, ritonavir, sertraline augmenting continuous low dose temozolomide

CUSP9 treatment protocol for recurrent glioblastoma was published one year ago. We now present a slight modification, designated CUSP9*. CUSP9* drugs--aprepitant, artesunate, auranofin, captopril, celecoxib, disulfiram, itraconazole, sertraline, ritonavir, are all widely approved by regulatory authorities, marketed for non-cancer indications. Each drug inhibits one or more important growth-enhancing pathways used by glioblastoma. By blocking survival paths, the aim is to render temozolomide, the current standard cytotoxic drug used in primary glioblastoma treatment, more effective. Although esthetically unpleasing to use so many drugs at once, the closely similar drugs of the original CUSP9 used together have been well-tolerated when given on a compassionate-use basis in the cases that have come to our attention so far. We expect similarly good tolerability for CUSP9*. The combined action of this suite of drugs blocks signaling at, or the activity of, AKT phosphorylation, aldehyde dehydrogenase, angiotensin converting enzyme, carbonic anhydrase -2,- 9, -12, cyclooxygenase-1 and -2, cathepsin B, Hedgehog, interleukin-6, 5-lipoxygenase, matrix metalloproteinase -2 and -9, mammalian target of rapamycin, neurokinin-1, p-gp efflux pump, thioredoxin reductase, tissue factor, 20 kDa translationally controlled tumor protein, and vascular endothelial growth factor. We believe that given the current prognosis after a glioblastoma has recurred, a trial of CUSP9* is warranted.