ESMO recommendations on the standard methods to detect NTRK fusions in daily practice and clinical research

Abstract Background NTRK1, NTRK2 and NTRK3 fusions are present in a plethora of malignancies across different histologies. These fusions represent the most frequent mechanism of oncogenic activation of these receptor tyrosine kinases, and biomarkers for the use of TRK small molecule inhibitors. Given the varying frequency of NTRK1/2/3 fusions, crucial to the administration of NTRK inhibitors is the development of optimal approaches for the detection of human cancers harbouring activating NTRK1/2/3 fusion genes. Materials and methods Experts from several Institutions were recruited by the European Society for Medical Oncology (ESMO) Translational Research and Precision Medicine Working Group (TR and PM WG) to review the available methods for the detection of NTRK gene fusions, their potential applications, and strategies for the implementation of a rational approach for the detection of NTRK1/2/3 fusion genes in human malignancies. A consensus on the most reasonable strategy to adopt when screening for NTRK fusions in oncologic patients was sought, and further reviewed and approved by the ESMO TR and PM WG and the ESMO leadership. Results The main techniques employed for NTRK fusion gene detection include immunohistochemistry, fluorescence in situ hybridization (FISH), RT-PCR, and both RNA-based and DNA-based next generation sequencing (NGS). Each technique has advantages and limitations, and the choice of assays for screening and final diagnosis should also take into account the resources and clinical context. Conclusion In tumours where NTRK fusions are highly recurrent, FISH, RT-PCR or RNA-based sequencing panels can be used as confirmatory techniques, whereas in the scenario of testing an unselected population where NTRK1/2/3 fusions are uncommon, either front-line sequencing (preferentially RNA-sequencing) or screening by immunohistochemistry followed by sequencing of positive cases should be pursued

ESTIMATING GENOME-WIDE COPY NUMBER USING ALLELE SPECIFIC MIXTURE MODELS

Genomic changes such as copy number alterations are thought to be one of the major underlying causes of human phenotypic variation among normal and disease subjects [23,11,25,26,5,4,7,18]. These include chromosomal regions with so-called copy number alterations: instead of the expected two copies, a section of the chromosome for a particular individual may have zero copies (homozygous deletion), one copy (hemizygous deletions), or more than two copies (amplifications). The canonical example is Down syndrome which is caused by an extra copy of chromosome 21. Identification of such abnormalities in smaller regions has been of great interest, because it is believed to be an underlying cause of cancer. More than one decade ago comparative genomic hybridization (CGH)technology was developed to detect copy number changes in a high-throughput fashion. However, this technology only provides a 10 MB resolution which limits the ability to detect copy number alterations spanning small regions. It is widely believed that a copy number alteration as small as one base can have significant downstream effects, thus microarray manufacturers have developed technologies that provide much higher resolution. Unfortunately, strong probe effects and variation introduced by sample preparation procedures have made single-point copy number estimates too imprecise to be useful. CGH arrays use a two-color hybridization, usually comparing a sample of interest to a reference sample, which to some degree removes the probe effect. However, the resolution is not nearly high enough to provide single-point copy number estimates. Various groups have proposed statistical procedures that pool data from neighboring locations to successfully improve precision. However, these procedure need to average across relatively large regions to work effectively thus greatly reducing the resolution. Recently, regression-type models that account for probe-effect have been proposed and appear to improve accuracy as well as precision. In this paper, we propose a mixture model solution specifically designed for single-point estimation, that provides various advantages over the existing methodology. We use a 314 sample database, constructed with public datasets, to motivate and fit models for the conditional distribution of the observed intensities given allele specific copy numbers. With the estimated models in place we can compute posterior probabilities that provide a useful prediction rule as well as a confidence measure for each call. Software to implement this procedure will be available in the Bioconductor oligo packagehttp://www.bioconductor.org)

Ab Initio Potential Energy Surfaces for SiH_2 ^+

Abstract. With the advent of next generation sequencing technologies, the cost of sequencing whole genomes is poised to go below $1000 per human individual in a few years. As more and more genomes are sequenced, analysis methods are undergoing rapid development, making it tempting to store sequencing data for long periods of time so that the data can be re-analyzed with the latest techniques. The challenging open research problems, huge influx of data, and rapidly improving analysis techniques have created the need to store and transfer very large volumes of data. Compression can be achieved at many levels, including trace level (image data), sequence level (compressing a non-redundant genomic sequence), and fragmentlevel, which involves compressing a set of short, and redundant collection of fragment reads, along with quality-values on the base-calls. We focus on fragmentlevel compression, which is the pressing need today. Our paper makes two contributions, implemented in a tool, SLIMGENE. First, we introduce a set of domain specific loss-less compression schemes that achieve over 40 × compression of fragments, outperforming bzip2 by over 6×. Including quality values, we show a 5 × compression using less running time than bzip2. Second, given the discrepancy between the compression factor obtained with and without quality values, we initiate the study of using ‘lossy ’ quality values. Specifically, we show that a lossy quality value quantization results in 14× compression but has minimal impact on downstream applications like SNP calling that use the quality values. Discrepancies between SNP calls made between the lossy and lossless versions of the data are limited to low coverage areas where even the SNP calls made by the lossless version are marginal.

Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq.

Tumor subclasses differ according to the genotypes and phenotypes of malignant cells as well as the composition of the tumor microenvironment (TME). We dissected these influences in isocitrate dehydrogenase (IDH)-mutant gliomas by combining 14,226 single-cell RNA sequencing (RNA-seq) profiles from 16 patient samples with bulk RNA-seq profiles from 165 patient samples. Differences in bulk profiles between IDH-mutant astrocytoma and oligodendroglioma can be primarily explained by distinct TME and signature genetic events, whereas both tumor types share similar developmental hierarchies and lineages of glial differentiation. As tumor grade increases, we find enhanced proliferation of malignant cells, larger pools of undifferentiated glioma cells, and an increase in macrophage over microglia expression programs in TME. Our work provides a unifying model for IDH-mutant gliomas and a general framework for dissecting the differences among human tumor subclasses