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Molecular profiling of glioblastoma has revealed complex cytogenetic, epigenetic, and molecular abnormalities that are necessary for diagnosis, prognosis, and treatment. Our neuro-oncology group has developed a data-driven, institutional consensus guideline for efficient and optimal workup of glioblastomas based on our routine performance of molecular testing. We describe our institution’s testing algorithm, assay development, and genetic findings in glioblastoma, to illustrate current practices and challenges in neuropathology related to molecular and genetic testing. We have found that coordination of test requisition, tissue handling, and incorporation of results into the final pathologic diagnosis by the neuropathologist improve patient care. Here, we present analysis of O 6 -methylguanine-DNA-methyltransferase promoter methylation and next-generation sequencing results of 189 patients, obtained utilizing our internal processes led by the neuropathology team. Our institutional pathway for neuropathologist-driven molecular testing has streamlined the management of glioblastoma samples for efficient return of results for incorporation of genomic data into the pathological diagnosis and optimal patient care.

BackgroundNext‐generation sequencing (NGS) of surgically resected solid tumor samples has become integral to personalized medicine approaches for cancer treatment and monitoring. Liquid biopsies, or the enrichment and characterization of circulating tumor cells (CTCs) from blood, can provide noninvasive detection of evolving tumor mutations to improve cancer patient care. However, the application of solid tumor NGS approaches to circulating tumor samples has been hampered by the low‐input DNA available from rare CTCs. Moreover, whole genome amplification (WGA) approaches used to generate sufficient input DNA are often incompatible with blood collection tube preservatives used to facilitate clinical sample batching.MethodsTo address this, we have developed a novel approach combining tumor cell isolation from preserved blood with Repli‐G WGA and Illumina TruSeq Amplicon Cancer Panel‐based NGS. We purified cell pools ranging from 10 to 1000 cells from three different cell lines, and quantitatively demonstrate comparable quality of DNA extracted from preserved versus unpreserved samples.ResultsPreservation and WGA were compatible with the generation of high‐quality libraries. Known point mutations and gene amplification were detected for libraries that had been prepared from amplified DNA from preserved blood.ConclusionThese spiking experiments provide proof of concept of a clinically applicable workflow for real‐time monitoring of patient tumor using noninvasive liquid biopsies.

Tumor endothelial marker 1 (TEM1) has been identified as a novel surface marker upregulated on the blood vessels and stroma in many solid tumors. We previously isolated a novel single-chain variable fragment (scFv) 78 against TEM1 from a yeast display scFv library. Here, we evaluated the potential applications of scFv78 as a tool for tumor molecular imaging, immunotoxin-based therapy and nanotherapy. Epitope mapping, three-dimensional structure docking and affinity measurements indicated that scFv78 could bind to both human and murine TEM1, with equivalent affinity, at a well-conserved conformational epitope. The rapid internalization of scFv78 and scFv78-labeled nanoparticles was triggered after specific TEM1 binding. The scFv78-saporin immunoconjugate also exerted dose-dependent cytotoxicity with high specificity to TEM1-positive cells in vitro. Finally, specific and sensitive tumor localization of scFv78 was confirmed with optical imaging in a tumor mouse model that has highly endogenous mTEM1 expression in the vasculature. Our data indicated that scFv78, the first fully human anti-TEM1 recombinant antibody, recognizes both human and mouse TEM1 and has unique and favorable features that are advantageous for the development of imaging probes or antibody-toxin conjugates for a large spectrum of human TEM1-positive solid tumors.

The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner. To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics. To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases were conducted. Potential associations were identified between several imaging features and nine genes: WT1, LGI3, SP7, DSG1, ORM1, CLDN10, CST1, SMTNL2 and SLC22A31 for IBC, and eight genes: NR0B1, PLA2G2A, MAL, CLDN16, PRDM14, VRTN, LRRN1 and MECOM for HNSCC. In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centre point for an emerging radiogenomic knowledge base. Software availability www.ImaGene.pgxguide.org, https://github.com/skr1/Imagene.git Supplementary Materials Supplementary Materials are available at https://github.com/skr1/Imagene.git