Isocitrate dehydrogenase 1 mutation subtypes at site 132 and their translational potential in glioma

Rapid IDH1-R132 genotyping panel utilizing locked nucleic acid loop-mediated isothermal amplification

While the detection of single-nucleotide variants (SNVs) is important for evaluating human health and disease, most genotyping methods require a nucleic acid extraction step and lengthy analytical times. Here, we present a protocol which utilizes the integration of locked nucleic acids (LNAs) into self-annealing loop primers for the allelic discrimination of five isocitrate dehydrogenase 1 R132 (IDH1-R132) variants using loop-mediated isothermal amplification (LAMP). This genotyping panel was initially evaluated using purified synthetic DNA to show proof of specific SNV discrimination. Additional evaluation using glioma tumor lysates with known IDH1-R132 mutational status demonstrated specificity in approximately 35 min without the need for a nucleic acid extraction purification step. This LNA-LAMP-based genotyping assay can detect single base differences in purified nucleic acids or tissue homogenates, including instances where the variant of interest is present in an excess of background wild-type DNA. The pH-based colorimetric indicator of LNA-LAMP facilitates convenient visual interpretation of reactions, and we demonstrate successful translation to an end-point format using absorbance ratio, allowing for an alternative and objective approach for differentiating between positive and negative reactions. Importantly, the LNA-LAMP genotyping panel is highly reproducible, with no false-positive or false-negative results observed.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Characteristics of IDH-mutant gliomas with non-canonical IDH mutation

BACKGROUND

Approximately 10% of IDH-mutant gliomas harbour non-canonical IDH mutations (non-p.R132H IDH1 and IDH2 mutations).

OBJECTIVE

The aim of this study was to analyse the characteristics of non-canonical IDH-mutant gliomas.

MATERIALS AND METHODS

We retrospectively analysed the characteristics of 166 patients with non-canonical IDH mutant gliomas and compared them to those of 155 consecutive patients with IDH1 p.R132H mutant gliomas.

RESULTS

The median age at diagnosis was 38 years in patients with non-canonical IDH mutant gliomas and 43 years in glioma patients with IDH1 p.R132H-mutant tumours. Family history of cancer was more frequent among glioma patients harbouring non-canonical IDH mutations than in patients with IDH1 p.R132H mutations (22.2% vs 5.1%; P < 0.05). Tumours were predominantly localised in the frontal lobe regardless of the type of IDH mutation. Compared to IDH1 p.R132H-mutant gliomas, tumours with non-canonical IDH mutations were more frequently found in the infratentorial region (5.5% vs 0%; P < 0.05) and were often multicentric (4.8% vs 0.9%; P < 0.05). Compared to IDH1 P.R132H-mutant gliomas, tumours with non-canonical IDH1 mutations were more frequently astrocytomas (65.6% vs 43%, P < 0.05), while those with IDH2 mutations were more frequently oligodendrogliomas (85% vs 48.3%; P < 0.05). The median overall survival was similar in patients with IDH1 p.R132H-mutant gliomas and patients with non-canonical IDH-mutant gliomas.

CONCLUSION

Gliomas with non-canonical IDH mutations have distinct radiological and histological characteristics. The presence of such tumours seems to be associated with genetic predisposition to cancer development.

A pan-cancer analysis of the clinical and genetic portraits of somatostatin receptor expressing tumor as a potential target of peptide receptor imaging and therapy

Purpose

Although somatostatin receptor (SST) is a promising theranostic target and is widely expressed in tumors of various organs, the indication for therapies targeting SST is limited to typical gastroenteropancreatic neuroendocrine tumors (NETs). Thus, broadening the scope of the current clinical application of peptide receptor radiotherapy (PRRT) can be supported by a better understanding of the landscape of SST-expressing tumors.

Methods

SST expression levels were assessed in data from The Cancer Genome Atlas across 10,701 subjects representing 32 cancer types. As the major target of PRRT is SST subtype 2 (SST2), correlation analyses between the pan-cancer profiles, including clinical and genetic features, and SST2 level were conducted. The median SST2 expression level of pheochromocytoma and paraganglioma (PCPG) samples was used as the threshold to define “high-SST2 tumors.” The prognostic value of SST2 in each cancer subtype was evaluated by using Cox proportional regression analysis.

Results

We constructed a resource of SST expression patterns associated with clinicopathologic features and genomic alterations. It provides an interactive tool to analyze SST expression patterns in various cancer types. As a result, eight of the 31 cancer subtypes other than PCPG had more than 5% of tumors with high-SST2 expression. Low-grade glioma (LGG) showed the highest proportion of high-SST2 tumors, followed by breast invasive carcinoma (BRCA). LGG showed different SST2 levels according to tumor grade and histology. IDH1 mutation was significantly associated with high-SST2 status. In BRCA, the SST2 level was different according to the hormone receptor status. High-SST2 status was significantly associated with good prognosis in LGG patients. High-SST2 status showed a trend for association with poor prognosis in triple-negative breast cancer subjects.

Conclusion

A broad range of SST2 expression was observed across diverse cancer subtypes. The SST2 expression level showed a significant association with genomic and clinical aspects across cancers, especially in LGG and BRCA. These findings extend our knowledge base to diversify the indications for PRRT as well as SST imaging.

A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 232 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 76% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore, providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patient's genetic predisposition interacts with the effect of somatic mutations on survival outcomes in some cancers.