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BACKGROUND - Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level.
RESULTS - We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as "repertoire fingerprinting." We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses.
CONCLUSIONS - Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.
Minichromosome maintenance protein 10 (Mcm10) is essential for DNA unwinding by the replisome during S phase. It is emerging as a promising anti-cancer target as MCM10 expression correlates with tumour progression and poor clinical outcomes. Here we used a competition-based fluorescence polarization (FP) high-throughput screening (HTS) strategy to identify compounds that inhibit Mcm10 from binding to DNA. Of the five active compounds identified, only the anti-parasitic agent suramin exhibited a dose-dependent decrease in replication products in an in vitro replication assay. Structure-activity relationship evaluation identified several suramin analogues that inhibited ssDNA binding by the human Mcm10 internal domain and full-length Xenopus Mcm10, including analogues that are selective for Mcm10 over human RPA. Binding of suramin analogues to Mcm10 was confirmed by surface plasmon resonance (SPR). SPR and FP affinity determinations were highly correlated, with a similar rank between affinity and potency for killing colon cancer cells. Suramin analogue NF157 had the highest human Mcm10 binding affinity (FP K 170 nM, SPR K 460 nM) and cell activity (IC 38 µM). Suramin and its analogues are the first identified inhibitors of Mcm10 and probably block DNA binding by mimicking the DNA sugar phosphate backbone due to their extended, polysulfated anionic structures.
Human genomic sequencing has potential diagnostic, prognostic, and therapeutic value across a wide breadth of clinical disciplines. One barrier to widespread adoption is the paucity of evidence for improved outcomes in patients who do not already have an indication for more focused testing. In this Series paper, we review clinical outcome studies in genomic medicine and discuss the important features and key challenges to building evidence for next generation sequencing in the context of routine patient care.
Copyright © 2019 Elsevier Ltd. All rights reserved.
Using an ORF kinome screen in MCF-7 cells treated with the CDK4/6 inhibitor ribociclib plus fulvestrant, we identified FGFR1 as a mechanism of drug resistance. FGFR1-amplified/ER+ breast cancer cells and MCF-7 cells transduced with FGFR1 were resistant to fulvestrant ± ribociclib or palbociclib. This resistance was abrogated by treatment with the FGFR tyrosine kinase inhibitor (TKI) lucitanib. Addition of the FGFR TKI erdafitinib to palbociclib/fulvestrant induced complete responses of FGFR1-amplified/ER+ patient-derived-xenografts. Next generation sequencing of circulating tumor DNA (ctDNA) in 34 patients after progression on CDK4/6 inhibitors identified FGFR1/2 amplification or activating mutations in 14/34 (41%) post-progression specimens. Finally, ctDNA from patients enrolled in MONALEESA-2, the registration trial of ribociclib, showed that patients with FGFR1 amplification exhibited a shorter progression-free survival compared to patients with wild type FGFR1. Thus, we propose breast cancers with FGFR pathway alterations should be considered for trials using combinations of ER, CDK4/6 and FGFR antagonists.
Insertional mutagenesis is an important risk with all genetically modified cell therapies, including chimeric antigen receptor (CAR)-T cell therapy used for hematological malignancies. Here we describe a new tagmentation-assisted PCR (tag-PCR) system that can determine the integration sites of transgenes without using restriction enzyme digestion (which can potentially bias the detection) and allows library preparation in fewer steps than with other methods. Using this system, we compared the integration sites of CD19-specific CAR genes in final T cell products generated by retrovirus-based and lentivirus-based gene transfer and by the piggyBac transposon system. The piggyBac system demonstrated lower preference than the retroviral system for integration near transcriptional start sites and CpG islands and higher preference than the lentiviral system for integration into genomic safe harbors. Integration into or near proto-oncogenes was similar in all three systems. Tag-PCR mapping is a useful technique for assessing the risk of insertional mutagenesis.
Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Functional disruptions of susceptibility genes by large genomic structure variant (SV) deletions in germlines are known to be associated with cancer risk. However, few studies have been conducted to systematically search for SV deletions in breast cancer susceptibility genes. We analysed deep (> 30x) whole-genome sequencing (WGS) data generated in blood samples from 128 breast cancer patients of Asian and European descent with either a strong family history of breast cancer or early cancer onset disease. To identify SV deletions in known or suspected breast cancer susceptibility genes, we used multiple SV calling tools including Genome STRiP, Delly, Manta, BreakDancer and Pindel. SV deletions were detected by at least three of these bioinformatics tools in five genes. Specifically, we identified heterozygous deletions covering a fraction of the coding regions of BRCA1 (with approximately 80kb in two patients), and TP53 genes (with ∼1.6 kb in two patients), and of intronic regions (∼1 kb) of the PALB2 (one patient), PTEN (three patients) and RAD51C genes (one patient). We confirmed the presence of these deletions using real-time quantitative PCR (qPCR). Our study identified novel SV deletions in breast cancer susceptibility genes and the identification of such SV deletions may improve clinical testing.
Multiplex genomic profiling is standard of care for patients with advanced lung adenocarcinomas. The Lung Cancer Mutation Consortium (LCMC) is a multi-institutional effort to identify and treat oncogenic driver events in patients with lung adenocarcinomas. Sixteen U.S. institutions enrolled 1,367 patients with lung cancer in LCMC2; 904 were deemed eligible and had at least one of 14 cancer-related genes profiled using validated methods including genotyping, massively parallel sequencing, and IHC. The use of targeted therapies in patients with or p.V600E mutations, , or rearrangements, or amplification was associated with a survival increment of 1.5 years compared with those with such mutations not receiving targeted therapy, and 1.0 year compared with those lacking a targetable driver. Importantly, 60 patients with a history of smoking derived similar survival benefit from targeted therapy for alterations in //, when compared with 75 never smokers with the same alterations. In addition, coexisting mutations were associated with shorter survival among patients with , or alterations. Patients with adenocarcinoma of the lung and an oncogenic driver mutation treated with effective targeted therapy have a longer survival, regardless of prior smoking history. Molecular testing should be performed on all individuals with lung adenocarcinomas irrespective of clinical characteristics. Routine use of massively parallel sequencing enables detection of both targetable driver alterations and tumor suppressor gene and other alterations that have potential significance for therapy selection and as predictive markers for the efficacy of treatment. .
©2017 American Association for Cancer Research.
BACKGROUND - High throughput sequencing technology enables the both the human genome and transcriptome to be screened at the single nucleotide resolution. Tools have been developed to infer single nucleotide variants (SNVs) from both DNA and RNA sequencing data. To evaluate how much difference can be expected between DNA and RNA sequencing data, and among tissue sources, we designed a study to examine the single nucleotide difference among five sources of high throughput sequencing data generated from the same individual, including exome sequencing from blood, tumor and adjacent normal tissue, and RNAseq from tumor and adjacent normal tissue.
RESULTS - Through careful quality control and analysis of the SNVs, we found little difference between DNA-DNA pairs (1%-2%). However, between DNA-RNA pairs, SNV differences ranged anywhere from 10% to 20%.
CONCLUSIONS - Only a small portion of these differences can be explained by RNA editing. Instead, the majority of the DNA-RNA differences should be attributed to technical errors from sequencing and post-processing of RNAseq data. Our analysis results suggest that SNV detection using RNAseq is subject to high false positive rates.
We hypothesize that the relative mitochondria copy number (MTCN) can be estimated by comparing the abundance of mitochondrial DNA to nuclear DNA reads using high throughput sequencing data. To test this hypothesis, we examined relative MTCN across 13 breast cancer cell lines using the RT-PCR based NovaQUANT Human Mitochondrial to Nuclear DNA Ratio Kit as the gold standard. Six distinct computational approaches were used to estimate the relative MTCN in order to compare to the RT-PCR measurements. The results demonstrate that relative MTCN correlates well with the RT-PCR measurements using exome sequencing data, but not RNA-seq data. Through analysis of copy number variants (CNVs) in The Cancer Genome Atlas, we show that the two nuclear genes used in the NovaQUANT assay to represent the nuclear genome often experience CNVs in tumor cells, questioning the accuracy of this gold-standard method when it is applied to tumor cells.
Copyright © 2017 Elsevier Inc. All rights reserved.