Is Personalized Medicine Here?

Clinical application of next-generation sequencing

The widespread availability of genomic analysis of tumors in clinical practice has been primarily driven by the advent of next-generation sequencing (NGS). The term “NGS” includes several types of genomic sequencing methodologies, such as targeted sequencing or “hotspot panels,” whole-exome sequencing (WES), whole-genome sequencing (WGS), RNA sequencing (RNA-seq) (transcriptome), and bisulfite sequencing. For clinical applications, hotspot panels—as they have commonly become known—are rapidly becoming part of standard practice. These panels utilize massive multiplexed parallel sequencing to target predefined areas of the genome. The two most well-known NGS manufacturers, Ion Torrent and Illumina, offer the AmpliSeq panels and TruSeq panels, respectively, for targeted sequencing. The panels and associated library preparation kits can range in size from 20 to 400 genetic alterations (including insertions and deletions), but the most commonly used panels contain approximately 20 to 60 cancer-related genes and can utilize DNA from any source, including paraffin-embedded tissue. Although each company uses a fundamentally different technology to perform sequencing (semiconductor chips for Ion Torrent vs fluorescence detection for Illumina), analytic performance is very similar for the two platforms, with the exception of Ion Torrent’s ability to resolve homopolymers, which makes it less suitable for detecting large insertions. The most significant differences between the platforms are associated with cost and throughput specifics that are more relevant to laboratory management than to clinical decisions.

rna-seq

Two other key applications of NGS technology are RNA-seq and bisulfite sequencing for epigenomic profiling. Both applications are less commonly used than the targeted sequencing panels discussed above. Regarding bisulfite/methylation analysis: this is routinely used in the context of O6-methylguanine-DNA methyltransferase promoter methylation in glioblastoma but utilizes a pyrosequencing assay. Methylation analysis by NGS is performed using a method called bisulfite amplicon sequencing and is still very much relegated to the research setting. However, preliminary clinical applications have revealed the identification of genome-wide hypomethylation from the serum of patients with various malignancies.

RNA-seq data, often referred to as the transcriptome, have the potential for incredible clinical benefits because they can provide information on the presence of gene fusion products, as well as on gene amplification or downregulation. RNA-seq also provides a more functional understanding of the genome than does mutational analysis. The most basic example of this is through quantification of gene expression, but it can also identify and quantify specific gene isoforms that can have therapeutic ramifications. For example, vascular endothelial growth factor (VEGF)—commonly understood as a proangiogenic gene—has multiple isoforms with both proangiogenic and antiangiogenic activity. Such biologic nuances add complexity to targeted treatment, given that the effects of an inhibitor could in theory vary according to the expression of these isoforms in specific tissues. Gene isoform analysis could also advance the understanding of pathogenesis and the classification of cancer into additional subtypes that better predict outcomes.

The remarkable potential of this technology is balanced by numerous challenges. RNA-seq is limited by the fundamental fragility and instability of RNA derived from formalin-fixed, paraffin-embedded samples, which makes sequencing of RNA a far more challenging task than that of DNA. More important, though, is the biologic complexity. Single-cell sequencing has demonstrated differential gene expression related to therapeutic resistance within the same tumor, and similar methodologies used in tumor microenvironment studies have shown extensive variability in stromal and immune cells that can mediate processes such as immune surveillance, angiogenesis, and metastasis.DNA sequencing by NGS cannot capture this type of information, which is critical to understanding the tumor microenvironment. However, trying to resolve this heterogeneity in a clinical setting that cannot support such time- and resource-intensive methodologies is prohibitive to the use of RNA-seq. For example, in mutational analysis, heavy inflammatory cell infiltration into a tumor decreases the mutation allele frequency detected by sequencing because there is less total tumor DNA. But in RNA-seq, such infiltration could potentially lead to inaccurate conclusions about the actual cancer cells’ transcriptome expression.

In general, as analysis methods become exceedingly more granular at the biologic level, the ability to extrapolate to the phenotypic level becomes inherently limited. As we will discuss in more depth later, integrated analysis methods that utilize multiple sources of data can potentially break through these limitations. Central to these approaches are advances in bioinformatics methods that make it possible to interpret the extensive data produced by such analyses. In the case of tumor heterogeneity, significant progress has been made in resolving mutational heterogeneity for the evaluation of clonal evolution, but these methods are limited to WES and WGS data, which are rarely used in the clinical setting. Somewhat similar methods of deconvoluting gene expression profiles of individual cell types in heterogeneous tissue samples also exist, but the methods are arguably even more computationally intensive and are still in the early stages of development. Nevertheless, major efforts have been directed to overcoming these challenges, and promising solutions that are in development could lead to clinical translation in the near future. Widespread adoption of RNA-seq would likely lead to many new insights into treatment resistance and could play an important role in the treatment of patients.

The restricted capacity of NGS platforms for the capture of alterations in epithelial-stromal interactions, angiogenesis, and immune modulation represents another major limitation. However, emerging evidence shows that the mutational profile of cancer cells influences communication with the surrounding environment. For example, patients with BRAF-mutant melanoma treated with BRAF inhibitors exhibited increased tumor infiltration by CD4+ and CD8+ lymphocytes on sequential tumor samples; the level of intratumoral CD8+ lymphocytes correlated with a reduction in tumor size and an increase in necrosis in posttreatment biopsies. It has also been shown that the beta-catenin pathway in melanoma cells is associated with a lack of T-cell infiltration and resistance to anti–programmed death ligand 1 (PD-L1) and anti–cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) treatments. These interactions between tumor genomic aberrations and the cancer immune microenvironment provide the impetus for clinical trials that incorporate advanced diagnostics concepts to guide therapeutic approaches, including sequencing of targeted therapies and immune checkpoint inhibitors.

Source – Cancer Network

Benedito A. Carneiro BA, Costa R, Taxter T, Chandra S, Chae YK, Cristofanilli M, Giles FJ. (2016) Is Personalized Medicine Here? Oncology Journal [Epub ahead of print]. [article]

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