Sameer Agnihotri

Intertumoral Heterogeneity within Medulloblastoma Subgroups

While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.

Spatial heterogeneity in medulloblastoma

Spatial heterogeneity of transcriptional and genetic markers between physically isolated biopsies of a single tumor poses major barriers to the identification of biomarkers and the development of targeted therapies that will be effective against the entire tumor. We analyzed the spatial heterogeneity of multiregional biopsies from 35 patients, using a combination of transcriptomic and genomic profiles. Medulloblastomas (MBs), but not high-grade gliomas (HGGs), demonstrated spatially homogeneous transcriptomes, which allowed for accurate subgrouping of tumors from a single biopsy. Conversely, somatic mutations that affect genes suitable for targeted therapeutics demonstrated high levels of spatial heterogeneity in MB, malignant glioma, and renal cell carcinoma (RCC). Actionable targets found in a single MB biopsy were seldom clonal across the entire tumor, which brings the efficacy of monotherapies against a single target into question. Clinical trials of targeted therapies for MB should first ensure the spatially ubiquitous nature of the target mutation.

PARK2 Depletion Connects Energy and Oxidative Stress to PI3K/Akt Activation via PTEN S-Nitrosylation

Summary PARK2 is a gene implicated in disease states with opposing responses in cell fate determination, yet its contribution in pro-survival signaling is largely unknown. Here we show that PARK2 is altered in over a third of all human cancers, and its depletion results in enhanced phosphatidylinositol 3-kinase/Akt (PI3K/Akt) activation and increased vulnerability to PI3K/Akt/mTOR inhibitors. PARK2 depletion contributes to AMPK-mediated activation of endothelial nitric oxide synthase (eNOS), enhanced levels of reactive oxygen species, and a concomitant increase in oxidized nitric oxide levels, thereby promoting the inhibition of PTEN by S-nitrosylation and ubiquitination. Notably, AMPK activation alone is sufficient to induce PTEN S-nitrosylation in the absence of PARK2 depletion. Park2 loss and Pten loss also display striking cooperativity to promote tumorigenesis in vivo. Together, our findings reveal an important missing mechanism that might account for PTEN suppression in PARK2-deficient tumors, and they highlight the importance of PTEN S-nitrosylation in supporting cell survival and proliferation under conditions of energy deprivation.

Therapeutic radiation for childhood cancer drives structural aberrations of NF2 in meningiomas

Cranial radiotherapy improves survival of the most common childhood cancers, including brain tumors and leukemia. Unfortunately, long-term survivors are faced with consequences of secondary neoplasia, including radiation-induced meningiomas (RIMs). We characterized 31 RIMs with exome/NF2 intronic sequencing, RNA sequencing and methylation profiling, and found NF2 gene rearrangements in 12/31 of RIMs, an observation previously unreported in sporadic meningioma (SM). Additionally, known recurrent mutations characteristic of SM, including AKT1, KLF4, TRAF7 and SMO, were not observed in RIMs. Combined losses of chromosomes 1p and 22q were common in RIMs (16/18 cases) and overall, chromosomal aberrations were more complex than that observed in SM. Patterns of DNA methylation profiling supported similar cell of origin between RIMs and SMs. The findings indicate that the mutational landscape of RIMs is distinct from SMs, and have significant therapeutic implications for survivors of childhood cranial radiation and the elucidation of the molecular pathogenesis of meningiomas.Radiation-induced meningiomas are often more aggressive than sporadic ones. In this study, the authors perform an exome, methylation and RNA-seq analysis of 31 cases of radiation-induced meningioma and show NF2 rearrangement, an observation previously unreported in the sporadic tumors.

Serum lactate as a potential biomarker of malignancy in primary adult brain tumours

Lactate, a by-product of glycolysis, is an indicator of poor tissue perfusion and is a useful biomarker with prognostic value in risk-stratifying patients in several diseases. Furthermore, elevated lactate production is observed in tumour glycolysis, also known as the Warburg effect, and is essential in promoting tumour cell invasion, metastasis, and immune system evasion, promoting resistance to cell death. However, there are no studies of elevated serum lactate in brain tumour patients as a potential biomarker, to our knowledge. The aim of this study is to determine possible correlations between the malignancy of tumours and pre- and intraoperative serum lactate elevation in patients undergoing craniotomy for tumour resection. We provide initial evidence that a rise in serum lactate can be used as a non-invasive biomarker that correlates with brain tumour grade. The results from this study and future prospective studies may allow for determination of tumour progression and response to therapy using serum lactate as a biomarker.

Peptide vaccine immunotherapy biomarkers and response patterns in pediatric gliomas

Low-grade gliomas (LGGs) are the most common brain tumor affecting children. We recently reported an early phase clinical trial of a peptide-based vaccine, which elicited consistent antigen-specific T cell responses in pediatric LGG patients. Additionally, we observed radiologic responses of stable disease (SD), partial response (PR), and near-complete/complete response (CR) following therapy. To identify biomarkers of clinical response in peripheral blood, we performed RNA sequencing on PBMC samples collected at multiple time points. Patients who showed CR demonstrated elevated levels of T cell activation markers, accompanied by a cytotoxic T cell response shortly after treatment initiation. At week 34, patients with CR demonstrated both IFN signaling and Poly-IC:LC adjuvant response patterns. Patients with PR demonstrated a unique, late monocyte response signature. Interestingly, HLA-V expression, before or during therapy, and an early monocytic hematopoietic response were strongly associated with SD. Finally, low IDO1 and PD-L1 expression before treatment and early elevated levels of T cell activation markers were associated with prolonged progression-free survival. Overall, our data support the presence of unique peripheral immune patterns in LGG patients associated with different radiographic responses to our peptide vaccine immunotherapy. Future clinical trials, including our ongoing phase II LGG vaccine immunotherapy, should monitor these response patterns.

Molecular Signatures for Tumor Classification An Analysis of TCGA Data

Cancer classification in the clinic is primarily based on histological analysis in the proper clinical context, often supplemented by immunohistochemical and molecular studies. Recent genomic studies have shown the potential of integrated multiomics platforms for molecular classification. We performed unsupervised analyses of molecular platforms in The Cancer Genome Atlas data (nxa0=xa06,216 samples) in comparison with tumor type. Our data showed that mRNA signatures and DNA methylation signatures mapped to histological diagnosis with high accuracy (95% and 88%, respectively) as individual platforms. The accuracy of mRNA signatures alone for classification and subtype identification was comparable to accuracies reported in the previously published Pan-Cancer 12 analysis. When combined, mRNA and methylation revealed a set of outliers for which the mRNA- and methylation-based molecular signatures concordantly differed from the original histological diagnosis. A subset remained consistent as outliers after using alternative classification and clustering algorithms and analysis of an independent molecular platform (miRNA). Overall, our results indicate that unsupervised approaches with individual genomic platforms, especially gene expression and DNA methylation, provide substantial classification information and identify occasional outlier cases in which the molecular signature is distinct from signatures expected for a given histological diagnosis. Identification of cases in which the molecular signature correlates with a specific histology that differs from initial impressions may prompt reconsideration of tumor classification in specific cases.

Molecular Signatures for Tumor Classification: An Analysis of The Cancer Genome Atlas Data

Cancer classification in the clinic is primarily based on histological analysis in the proper clinical context, often supplemented by immunohistochemical and molecular studies. Recent genomic studies have shown the potential of integrated multiomics platforms for molecular classification. We performed unsupervised analyses of molecular platforms in The Cancer Genome Atlas data (nxa0=xa06,216 samples) in comparison with tumor type. Our data showed that mRNA signatures and DNA methylation signatures mapped to histological diagnosis with high accuracy (95% and 88%, respectively) as individual platforms. The accuracy of mRNA signatures alone for classification and subtype identification was comparable to accuracies reported in the previously published Pan-Cancer 12 analysis. When combined, mRNA and methylation revealed a set of outliers for which the mRNA- and methylation-based molecular signatures concordantly differed from the original histological diagnosis. A subset remained consistent as outliers after using alternative classification and clustering algorithms and analysis of an independent molecular platform (miRNA). Overall, our results indicate that unsupervised approaches with individual genomic platforms, especially gene expression and DNA methylation, provide substantial classification information and identify occasional outlier cases in which the molecular signature is distinct from signatures expected for a given histological diagnosis. Identification of cases in which the molecular signature correlates with a specific histology that differs from initial impressions may prompt reconsideration of tumor classification in specific cases.

Pre-clinical transgenic mouse models of nervous system tumors

The most common primary CNS tumors are gliomas, where other than a few subtypes such as oligodendrogliomas, the survival has remained unchanged despite advances in surgical, chemo- and radiation therapy, especially for the most malignant and common glioma; glioblastoma multiforme (GBM). Recent novel therapies like immuno- and gene therapy have shown some promise in existing pre-clinical models, but have failed to demonstrate therapeutic benefit in patients. The reason(s) for such failures include our incomplete understanding of the molecular pathogenesis of these tumors and also due to testing of novel biological therapies in less than ideal pre-clinical models, which for the most part have included xenografts established in mice from glioma cell lines or patient explants. Transgenic mouse models offers an opportunity to develop and utilize an easily replenished, reproducible, manipulated spontaneous and more appropriate pre-clinical model of human cancers. Here we highlight on how mouse models are generated using several techniques and how mouse models have come to the forefront to address several issues such as identifying novel tumour modifier genes of central and peripheral nervous system tumours. Lastly we discuss how mouse models may provide an invaluable tool in pre clinical drug screening and testing.