Next generation sequencing in cancer: opportunities and challenges for precision cancer medicine

Over the past decade, testing the genes of patients and their specific cancer types has become standardized practice in medical oncology since somatic mutations, changes in gene expression and epigenetic modifications are all hallmarks of cancer. However, while cancer genetic assessment has been limited to single biomarkers to guide the use of therapies, improvements in nucleic acid sequencing technologies and implementation of different genome analysis tools have enabled clinicians to detect these genomic alterations and identify functional and disease-associated genomic variants. Next-generation sequencing (NGS) technologies have provided clues about therapeutic targets and genomic markers for novel clinical applications when standard therapy has failed. While Sanger sequencing, an accurate and sensitive approach, allows for the identification of potential novel variants, it is however limited by the single amplicon being interrogated. Similarly, quantitative and qualitative profiling of gene expression changes also represents a challenge for the cancer field. Both RT-PCR and microarrays are efficient approaches, but are limited to the genes present on the array or being assayed. This leaves vast swaths of the transcriptome, including non-coding RNAs and other features, unexplored. With the advent of the ability to collect and analyze genomic sequence data in a timely fashion and at an ever-decreasing cost, many of these limitations have been overcome and are being incorporated into cancer research and diagnostics giving patients and clinicians new hope for targeted and personalized treatment. Below we highlight the various applications of next-generation sequencing in precision cancer medicine

Detection of activating estrogen receptor gene (ESR1) mutations in single circulating tumor cells

Purpose: Early detection is essential for treatment plans before onset of metastatic disease. Our purpose was to demonstrate feasibility to detect and monitor estrogen receptor 1 (ESR1) gene mutations at the single circulating tumor cell (CTC) level in metastatic breast cancer (MBC). Experimental Design: We used a CTC molecular characterization approach to investigate heterogeneity of 14 hotspot mutations in ESR1 and their correlation with endocrine resistance. Combining the CellSearch and DEPArray technologies allowed recovery of 71 single CTCs and 12 WBC from 3 ER-positive MBC patients. Forty CTCs and 12 WBC were subjected to whole genome amplification by MALBAC and Sanger sequencing. Results: Among 3 selected patients, 2 had an ESR1 mutation (Y537). One showed two different ESR1 variants in a single CTC and another showed loss of heterozygosity. All mutations were detected in matched cell-free DNA (cfDNA). Furthermore, one had 2 serial blood samples analyzed and showed changes in both cfDNA and CTCs with emergence of mutations in ESR1 (Y537S and T570I), which has not been reported previously. Conclusions: CTCs are easily accessible biomarkers to monitor and better personalize management of patients with previously demonstrated ER-MBC who are progressing on endocrine therapy. We showed that single CTC analysis can yield important information on clonal heterogeneity and can be a source of discovery of novel and potential driver mutations. Finally, we also validate a workflow for liquid biopsy that will facilitate early detection of ESR1 mutations, the emergence of endocrine resistance and the choice of further target therapy. ©2017 AACR

Impact of the number of comorbidities on cardiac sympathetic derangement in patients with reduced ejection fraction heart failure

Introduction Heart failure (HF) is frequently associated with comorbidities. 123I-metaiodobenzylguanidine (123I-mIBG) imaging constitutes an effective tool to measure cardiac adrenergic innervation and to improve prognostic stratification in HF patients, including the risk of major arrhythmic events. Although comorbidities have been individually associated with reduced cardiac adrenergic innervation, thus suggesting increased arrhythmic risk, very comorbid HF patients seem to be less likely to experience fatal arrhythmias. We evaluated the impact of the number of comorbidities on cardiac adrenergic innervation, assessed through 123I-mIBG imaging, in patients with systolic HF. Methods Patients with systolic HF underwent clinical examination, transthoracic echocardiography and cardiac 123I-mIBG scintigraphy. The presence of 7 comorbidities/conditions (smoking, chronic obstructive pulmonary disease, diabetes mellitus, peripheral artery disease, atrial fibrillation, chronic ischemic heart disease and chronic kidney disease) was documented in the overall study population. Results The study population consisted of 269 HF patients with a mean age of 66±11 years, a left ventricular ejection fraction (LVEF) of 31±7%, and 153 (57%) patients presented ≥3 comorbidities. Highly comorbid patients presented a reduced late heart to mediastinum (H/M) ratio, while no significant differences emerged in terms of early H/M ratio and washout rate. Multiple regression analysis revealed that the number of comorbidities was not associated with mIBG parameters of cardiac denervation, which were correlated with age, body mass index and LVEF. Conclusion In systolic HF patients, the number of comorbidities is not associated with alterations in cardiac adrenergic innervation. These results are consistent with the observation that very comorbid HF patients suffer lower risk of sudden cardiac death

Is quantitative real time polymerase chain reaction MCAM transcript assay really suitable for prognostic and predictive management of melanoma patients?

Background: Recent advances in next generation sequencing (NGS) technology have enabled comprehensive and accurate screening of the entire genomic region of BRCA1/2 genes and, to date, many studies report the effectiveness of these technologies. Here we show that Gene Scan (GS) labeling Quality Control (QC), performed before massive parallel pyrosequencing, coupled with Multiple Amplicon Quantification software (MAQ-S) analysis is a rapid and powerful tool in the detection of deleterious BRCA mutations carried by different patients. Methods: GS labeling QC assay was performed according to the manufacturers' instructions and MAQ-S software was employed for analysis results. Results: GS labeling QC was able to detect 14 different BRCA frameshift mutations in our patients. In addition, two novel BRCA mutations (c.1893_1894in5TTAAGCCCACAAAT in BRCA1 gene and c.9413_9414insT in BRCA2 gene) were identified. Conclusion: We prove that a simple QC step may represent a valid and useful tool for a rapid detection of frameshift mutations in BRCA genes. For this reason, we recommend using this approach before massive parallel sequencing. (C) 2014 Elsevier B.V. All rights reserved

A Targeted Mass Spectrometry Approach to Identify Peripheral Changes in Metabolic Pathways of Patients with Alzheimer’s Disease

: Alzheimer's disease (AD), a neurodegenerative disorder, is the most common cause of dementia in the elderly population. Since its original description, there has been intense debate regarding the factors that trigger its pathology. It is becoming apparent that AD is more than a brain disease and harms the whole-body metabolism. We analyzed 630 polar and apolar metabolites in the blood of 20 patients with AD and 20 healthy individuals, to determine whether the composition of plasma metabolites could offer additional indicators to evaluate any alterations in the metabolic pathways related to the illness. Multivariate statistical analysis showed that there were at least 25 significantly dysregulated metabolites in patients with AD compared with the controls. Two membrane lipid components, glycerophospholipids and ceramide, were upregulated, whereas glutamic acid, other phospholipids, and sphingolipids were downregulated. The data were analyzed using metabolite set enrichment analysis and pathway analysis using the KEGG library. The results showed that at least five pathways involved in the metabolism of polar compounds were dysregulated in patients with AD. Conversely, the lipid pathways did not show significant alterations. These results support the possibility of using metabolome analysis to understand alterations in the metabolic pathways related to AD pathophysiology

Tandem Mass Spectrometry as Strategy for the Selective Identification and Quantification of the Amyloid Precursor Protein Tyr682 Residue Phosphorylation Status in Human Blood Mononuclear Cells

Background: Alzheimer’s disease (AD) is a devastating neurodegenerative disease without guidelines for early diagnosis or personalized treatment. Previous studies have highlighted a crucial role of increasing phosphorylation levels of the amyloid precursor protein (APP) Tyr682 residue in predicting neuronal deficits in AD patients. However, the lack of a method for the identification and quantification of Tyr682 phosphorylation levels prevents its potential clinical applications. Methods: Here we report a method to identify and quantify APP Tyr682 phosphorylation levels in blood mononuclear cells of AD patients by tandem mass spectrometry (tMS). Results: This method showed excellent sensitivity with detection and quantification limits set respectively at 0.035 and 0.082 ng injected for the phosphorylated peptide and at 0.02 and 0.215 ng injected for the non-phosphorylated peptide. The average levels of both peptides were quantified in transfected HELA cells (2.48 and 3.53 ng/μg of protein, respectively). Preliminary data on 3 AD patients showed quantifiable levels of phosphorylated peptide (0.10–0.15 ng/μg of protein) and below the LOQ level of non-phosphorylated peptide (0.13 ng/μg of protein). Conclusion: This method could allow the identification of patients with increased APP Tyr682 phosphorylation and allow early characterization of molecular changes prior to the appearance of clinical signs

SARS-CoV-2 Subgenomic N (sgN) Transcripts in Oro-Nasopharyngeal Swabs Correlate with the Highest Viral Load, as Evaluated by Five Different Molecular Methods

Abstract: BackgroundThe COVID-19 pandemic has forced diagnostic laboratories to focus on the early diagnostics of SARS-CoV-2. The positivity of a molecular test cannot respond to the question regarding the viral capability to replicate, spread, and give different clinical effects. Despite the fact that some targets are covered by commercially-available assays, the identification of new biomarkers is desired in order to improve the quality of the information given by these assays. Therefore, since the subgenomic transcripts (sgN and sgE) are considered markers of viral activity, we evaluated these subgenomic transcripts in relation to the genomic amplification obtained using five different commercial CE-IVD tools. Methods: Five CE-IVD kits were compared in terms of their capability to detect both synthetic SARS-CoV-2 viral constructs (spiked in TMB or PBS medium) and targets (N, E, RdRp and Orf1ab genes) in twenty COVID-19–positive patients’ swabs. The sgN and sgE were assayed by real-time RT-qPCR and digital PCR. Results: None of the diagnostic kits missed the viral target genes when they were applied to targets spiked in TMB or PBS (at dilutions ranging from 100 pg to 0.1 pg). Nevertheless, once they were applied to RNA extracted from the patients’ swabs, the superimposability ranged from 50% to 100%, regardless of the extraction procedure. The sgN RNA transcript was detected only in samples with a higher viral load (Ct ≤ 22.5), while sgE was within all of the Ct ranges. Conclusions: The five kits show variable performances depending on the assay layout. It is worthy of note that the detection of the sgN transcript is associated with a higher viral load, thus representing a new marker of early and more severe infection