Abstract
The Glioblastoma (GB) field has been experiencing a therapeutic standstill since 2005, primarily due to ineffective preclinical approaches to test anti-cancer treatments. Therefore, better ...treatment screening approaches are required. We developed a NADH FLIM-based functional precision medicine approach, that within one week after surgery identified two groups of TMZ Responder and Non-Responder tumors. A 17 gene molecular signature able to classify with 100% precision the TMZ responder and non-responder samples was discovered and identified by Kaplan Meier analysis, a Low-Risk and a High-Risk survival group, interrogating the TCGA GB database (Hazard Ratio = 1.87 p=0.00098,n=148) and TCGA LGG database (Hazard Ratio = 7.66 p=1.197e−35, n=660). Same results were obtained in the CCGA datasets. The 17 gene signature power for independently predicting prognosis (Hazard Ratio = 1.9, p=0.002) was then confirmed by direct RNAseq analysis of a separate clinically characterized cohort of 235 GB patients. Then, we combined the methylation status of the MGMT promoter to analyze the survival status of patients. The survival analysis based on the 17 gene risk signature and MGMT promoter methylation status demonstrated remarkable stratification of the clinical courses into four subgroups. Patients with MGMT promoter unmethylation and 17 gene High-Risk score had the worst prognosis, while patients with MGMT promoter methylation and 17 gene Low-Risk score had the best prognosis. In the latter group, a significant 24-month increase in survival was observed with 10% of 100 months Long Survivors with a difference of 35 months compared to the other groups. Our data indicate a new statistically strong RNAseq-based prognostic survival and TMZ response tool for patients with malignant glioma. The accuracy of this functional precision medicine approach allowed the development of a new prognostic gene signature that can improve the clinical management of GB patients. The approach could be implemented on other cancers as well.
Abstract
Glioblastoma remains a highly malignant and intrinsically resistant brain tumor. Despite intensive research through which numerous potential druggable targets were identified, virtually all ...clinical trials of the past 20 years failed to improve the outcome for the vast majority of GBM patients. However, the identification of small subgroups of patients that showed an exceptional response across several trials, implies that, when selected more carefully, some GBM patients could probably still benefit from these therapies. Identifying these patients requires that suitable biomarkers are identified. In this project, we reassessed the molecular mechanisms of ten actionable compounds (selected from previously failed trials but for which exceptional responders had been observed) in a set of carefully selected patient-derived cell lines that were sensitive/resistant to the selected therapies. Moreover, to deal with tumor heterogeneity, we used a multi-omic functional precision oncology approach, combining scRNA-seq and CyTOF, to identify drug-specific biomarkers by comparing control and treated samples at single-cell resolution. By subsequently correlating the molecular signatures to eventual cytotoxicity profiles, we could identify intrinsically responsive tumor cells at the single-cell level within hours following drug exposure. Overall, this work lays the foundation for an actionable functional diagnostic assay that could help to identify eligible GBM patients in future clinical trials.
Abstract
BACKGROUND
Treatment-related toxicity is common in patients with glioblastoma (GBM) receiving chemotherapy and radiotherapy (RT). Temporalis muscle thickness (TMT) is a biomarker associated ...with sarcopenia and worse clinical outcomes in GBM, however its relation to treatment toxicity is less studied. We hypothesize that TMT may predict toxicity and survival in GBM patients.
METHODS
We reviewed consecutive patients with IDH-wildtype GBM treated from 2014-2019 at a single academic center. TMT was retrospectively assessed on T1-weighted MRI scans and dichotomized based upon previously validated sex-specific cutoff values. TMT was measured on baseline MRI scan at time of diagnosis. Cox regression multivariable analysis (MVA) was used to assess survival.
RESULTS
We evaluated 351 patients with median age of 60y (range 20-94) and median follow-up of 14mo. Most patients were male (59%), baseline KPS >70 (95%), and MGMT unmethylated (55%). After maximal safe resection, most patients received standard (90%) or hypofractionated (10%) RT with concurrent systemic therapy (89%). On MVA, baseline low TMT (HR 1.93, p=0.01), age >65y, baseline KPS, and MGMT-unmethylated status were associated with worse OS. On MVA, baseline low TMT (HR 1.95, p=0.01), age >65y, MGMT-unmethylated status, and discontinuing systemic therapy were associated with worse profession-free survival (PFS). 21 patients did not complete anticipated treatment course of chemoradiation and adjuvant systemic therapy due to toxicity, primarily thrombocytopenia, associated with worse OS on MVA (HR 1.99, p< 0.01). Low TMT was associated with higher risk of stopping treatment due to adverse events (OR 5.25, p< 0.01) independent of age, sex, extent of resection, RT dose on MVA.
CONCLUSION
Baseline low TMT was associated with worse PFS and OS, and it was associated with treatment interruption due to treatment toxicity in GBM patients. While further validation is needed, TMT may help identify patients who will benefit from aggressive symptom management or treatment deintensification.
Abstract
Aggressive meningiomas are prone to recur despite GTR and eventually progress: they represent a challenge and are difficult to recognize at first diagnosis. SOX2 (Sex-determining region ...Y-box2) is a transcription regulator whose role is crucial for cell’s fate and maintenance of progenitor’s identity during embryogenesis, and homeostasis and regeneration in adult tissue through stem cell activity preservation. We reviewed meningioma cases surgically treated at Gemelli Hospital, Rome between 2014 and 2019. We included all patients with diagnosis of grade 3 meningiomas, both progressive and de novo, grade 2 with at least one surgical recurrence after GTR and benign grade 1 and grade 2 without recurrence at 10 and 5 years long follow-up respectively. SOX2 expression was evaluated through IHC and RT-PCR. Its role in predicting progression, recurrence, OS and PFS was investigated. 87 patients were included: 16 de novo grade 3 meningioma, 7 progressive grade 1, 13 progressive grade 2, 12 recurrent grade 2, 20 benign grade 1, 19 benign grade 2. The IHC method for SOX2 was validated by correlation between IHC score and mRNA levels (Spearman R=0.0398, p=0.001, AUC 0.87). Although SOX2 expression is related to WHO grade in the series, its status doesn’t change with progression. SOX2 expression at first surgery is related to risk of progression (p< 0,0001) and represents a grade independent prognostic factor for PFS and OS (PFS 38,41 months in positive cases vs not reached in negative cases; p< 0,0001; OS 173,9 months in positive cases vs not reached in negative cases; p=0,0001) and both in grade 1 and grade 2. Histomorphological criteria, cornerstone of the current WHO classification, are inadequate to predict aggressiveness. SOX2 expression since first diagnosis is able to point out meningiomas prone to recur and progress. SOX2 status could integrate current classification as molecular biomarker of stemness and aggressiveness.
Abstract
The deregulation of canonical oncogenic pathways are largely responsible for driving pediatric cancers and can be targeted for therapeutics. Currently, we interrogate these pathways ...clinically by looking for gene mutations, but these are not found in all cases, and in others multiple genes are. We hypothesized that assessing transcriptomic and proteomic-based pathway activation will allow a better understanding of the most active oncogenic pathways and help guide therapy. To do this, we developed and validated a nanostring based assay that interrogates 4 key actionable pathways (MAPK, PI3K-AKT-mTOR, JAK-STAT, and NFkB) including RNA, protein and phosphoprotein expression. The assay was clinically validated using isogenic cell lines and a cohort of 40 gliomas with previous RNAseq. We then interrogated over 400 tumor samples, including 15 ependymomas, 11 medulloblastomas, 250 low grade gliomas (LGG), 145 high grade gliomas and 10 control normal brain specimens. Interestingly, although pediatric LGG exhibited higher MAPK activation than control tissue and other tumor types, a subset of these tumors have increased activity in PI3K , JAK and NFKB pathways. Furthermore, high PI3K activation score was correlated with worse PFS in a subset of pediatric LGGs that required adjuvant chemotherapy (p=0.018). To further explore the therapeutic implication of the assay, we analyzed a cohort of patients treated with MEK inhibitors (n=20). Strikingly, on top of universal RAS/MAPK activation, crosstalk between additional activated pathways such as PI3K and JAK-STAT may contribute to lack of response. In particular, pre-treatment and post-progression PLGG who failed therapy, revealed mild reduction in MAPK signature accompanied by increased PI3K phospho-proteins (p-S6/p-4EBP1,p-AKT)(p< 0.01). We conclude that assessing oncogenic pathway activation can add to DNA sequencing to predict different outcome and response to targeted therapies in childhood brain tumors. This can inform future therapeutic strategies including the identification of potential responders and combination strategies for non-responders.
Abstract
BACKGROUND
Invasive brain sampling is typically necessary for reliable diagnosis and prognostication of intra-axial brain tumors but carries risk of morbidity. Liquid biopsy of proximal ...fluids may mitigate this risk. Through direct contact with the tumor microenvironment and, as an ultra-filtrate of plasma, the cerebrospinal fluid may be the ideal matrix. Reflecting the tumor phenotype, proteomic analyses are critical. Here we identified diagnostic CSF proteomic signatures and putatively novel biomarkers for glioblastoma (GBM), brain metastases (BM), and central nervous system lymphoma (CNSL).
METHODS
CSF samples were retrospectively retrieved from the Penn State Neuroscience Biorepository and profiled using shotgun proteomics and the MStern approach. Proteomic signatures were identified using machine learning classifiers and survival analyses.
RESULTS
With as little as 30 µL of CSF, 755 unique proteins were recovered across 73 samples (22 GBM, 17 BM, 14 CNSL, 20 NPH). Proteomic-based classifiers identified malignancy with area under the receiver operating characteristic (AUROC) of 0.94 and distinguished between tumor entities with AUROC ≥0.95. More clinically relevant triplex classifiers, comprised of just 3 proteins, distinguished between tumor entities with AUROC of 0.75-0.89. Novel biomarkers were identified, including GAP43, TFF3 and CACNA2D2, and characterized using single-cell RNA sequencing.
DISCUSSION
Reliable classification of intra-axial malignancies using low CSF volumes is feasible, allowing for longitudinal tumor surveillance. Based on emerging evidence, upfront implantation of CSF reservoirs in brain tumor patients warrants consideration.
Abstract
Medulloblastoma is a central nervous system tumor that develops through various genetic, epigenetic, and non-coding (nc) RNA-related mechanisms, but the roles played by ncRNAs, particularly ...circular RNAs (circRNAs), remain poorly defined. CircRNAs are increasingly recognized as stable noncoding RNA therapeutic targets in many cancers, but little is known about their function, subtype specificity, and therapeutic potential in medulloblastomas. To determine medulloblastoma subgroup-specific circRNAs, we subjected RNA-seq data from 175 clinical medulloblastoma samples in four subgroups (SSH, WNT, G3, and G4) to a statistical and machine learning (random forest) pipeline and identified a group of medulloblastoma specific circular RNAs. CircRNA, circ_63706 was identified as sonic hedgehog (SHH) group specific and confirmed its expression by RNA-FISH analysis in clinical tissue samples (tissue microarrays). To identify the molecular function of circ_63706, we depleted circ_63706 in DAOY and ONS76 cells and subjected them to global RNA sequencing and lipid profiling. Circ_63706 resides in the coding gene Pericentrin (PCNT), which is known to be involved in congenital disorders. When Circ_63706 gets depleted by shRNA, it shows a significant decrease in cell proliferation and invasion in SSH cells, and mice implanted with circ_63706-deleted cells showed reduced tumor growth and extended survival compared to parental cells implant. At the molecular level, we identified circ_63706-deleted cells elevated total ceramide and oxidized lipids and reduced total triglyceride (TG). Our study implicates an identification of a novel oncogenic circular RNA in the medulloblastoma subgroup SSH and establishes its potential as a future therapeutic target.
Abstract
Methylation classification is an essential component for integrative diagnosis in glioma, however, the DNA methylation classification is not always available for all the samples. We ...hypothesized that Raman spectroscopy might be suitable to predict the glioma methylome, based upon its ability to create a molecular fingerprint of the tumor and would provide biological insights into the discriminatory features. Raman Spectroscopy was used for molecular fingerprinting of the regions of interest using 1mm2 FFPE tissue spots from 45 patient samples with LGm1 to LGm6 methylation subtypes. Spectral information was then used to train a convolutional neural network (CNN), capable of detecting the glioma methylation subtypes. 70 % of the dataset was used for model training while the remaining 30% for validation. We demonstrate that Raman spectroscopy can accurately and rapidly classify gliomas according to their methylation subtype from achieved FFPE samples, as a novel way to obtain classification. For each sample we ran Ward linkage clustering with a variable number of clusters (from 2 to 7), with the majority cluster corresponding to tumor spots and the others corresponding to (various types of) non-tumor spots. The average accuracy over all samples was 90:3%, the average precision was 99:6% and the average recall was 90:2%. We show that Raman spectroscopy together with artificial intelligence can predict the methylome of glioma samples and augment the ability to classify these tumors retrospectively. The non-destructive nature of this method and the ability to be applied on FFPE samples directly, allows the histopathologist to reuse of the same slide for subsequent staining and downstream analyses.
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