Zvi Ram

Comparative analysis of the NF2, TP53, PTEN, KRAS, NRAS and HRAS genes in sporadic and radiation-induced human meningiomas.

Irradiation to the head is associated with a significantly increased incidence of meningiomas. Radiation‐induced meningiomas morphologically resemble their sporadically arising counterparts; however, they frequently exhibit a more malignant phenotype. Several genes have been shown to carry mutations in meningiomas, with the NF2 gene being most frequently affected. To examine whether the NF2 gene also plays a role in the development of radiation‐induced meningiomas, we compiled a series of meningiomas from 25 patients with a history of previous cranial radiation. This series was compared with 21 atypical WHO grade II meningiomas and 15 anaplastic WHO grade III meningiomas, all from patients without a history of prior irradiation. NF2 mutations occurred significantly more often in sporadic atypical and anaplastic than in radiation‐induced meningiomas (p < 0.02). In addition, all meningiomas were examined for mutations in the PTEN, TP53, HRAS, KRAS and NRAS genes. Two mutations in the TP53 gene in a sporadic and a radiation‐induced tumor were detected. PTEN mutations were observed in 1 anaplastic and 1 radiation‐induced meningioma. No structural alterations were seen in the RAS genes. Our data suggest that, while there is a certain overlap in the mutational spectrum, NF2 mutations may not play such a prominent role in the pathogenesis of radiation‐induced compared to sporadic meningiomas.

Meningiomas induced by low-dose radiation carry structural variants of NF2 and a distinct mutational signature

Felix Sahm1,2 · Umut H. Toprak3 · Daniel Hübschmann3,4,5 · Kortine Kleinheinz3 · Ivo Buchhalter3 · Martin Sill6 · Damian Stichel2 · Matthias Schick7 · Melanie Bewerunge‐Hudler7 · Daniel Schrimpf1,2 · Gelareh Zadeh8,9 · Ken Aldape8,10 · Christel Herold‐Mende11 · Katja Beck12 · Ori Staszewski13 · Marco Prinz13,14 · Carmit Ben Harosh15 · Roland Eils3,4,12 · Dominik Sturm5,16 · David T. W. Jones16 · Stefan M. Pfister5,16 · Werner Paulus17 · Zvi Ram15,18 · Matthias Schlesner3 · Rachel Grossman15,18 · Andreas von Deimling1,2

Abstract 2272: Identification of predictive biomarkers for immunotherapy of brain and solid tumors by studying inter-cellular immune networks

Immune checkpoint molecules (ICMs) make up the immune suppressive network (ISN) that blocks spontaneous or therapy-induced anti-tumoral immune responses. A decade of research into checkpoint inhibitors (CPIs) has identified only few predictive biomarkers for single-agent CPI therapy. Those discovered show mediocre predictive capability. A considerable fraction of predicted non-responders demonstrate response and vice versa. It is unlikely that current biomarker discovery tools based on single antigen immunohistochemistry or whole-tumor genomics/transcriptomics will uncover predictive biomarkers for therapy with CPI combinations. Brain tumors, specifically glioblastoma, are almost uniformly lethal. Preclinical studies have shown that more than one agent is needed to relieve the strong immunosuppression in these tumors. No rational approach to select the right combination of agents is currently available. We have developed several unique methodologies that delineate the ISN formed between all immune and tumor cell subsets within individual human brain tumors. Our current ISN models incorporate cell-level data, frequency of all intratumoral immune subsets (up to 13 immune subsets), and gene-expression data with each subset9s deep-sequenced transcriptome. We produce transcriptomes from all intratumoral immune subsets (as few as 100 cells) and tumor cells that are sorted using elaborate multicolor flow cytometric panels. These transcriptomes reveal each subset9s full immune state: expression of all ICMs, all inter-cellular functional and communication molecules (e.g. cytokines, chemokines, receptors, ligands), and its active pathways. The collected data is integrated using systems-biology tools to generate models of the ISN which delineate the interactions that each cell subset has with all other immune or tumor cell subsets within a tumor. Individual ISN data can be evaluated in respect to each patient9s response to CPI treatment to determine which ISN patterns correlate with response, or lack of response, to CPI treatment. Emerging results may retrospectively explain the failure of some CPI clinical trials conducted on glioblastoma, and reveal new targets. Studying the relationship between ISN patterns and clinical responses to checkpoint inhibition may enable identification of much needed predictive biomarkers. A systematic evaluation of the suppressive components found to be frequently active in many patients9 ISNs may guide rational decisions in planning of clinical trials with improved chances to succeed. This methodology may, in the future, guide physicians in selecting reagents to treat an individual patient9s tumor, bringing about the age of personalized medicine to the field of immuno-oncology. Citation Format: Ilan Volovitz, Gil Diamant, Marina Roitman, Nati Shapira, Roni Hagai, Barak Bensimhon, Merav Lustgarten, Rachel Grossman, Zvi Ram. Identification of predictive biomarkers for immunotherapy of brain and solid tumors by studying inter-cellular immune networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2272.