Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing.

BACKGROUND: Advances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation. RESULTS: In this study, we compared the performance of NXT following Fluidigm's protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93-100% per cell). CONCLUSIONS: Our results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm's protocol using NXT.

Development and implementation of an automated and highly accurate reporting process for NGS-based clonality testing.

B and T cells undergo random recombination of the VH/DH/JH portions of the immunoglobulin loci (B cell) and T-cell receptors before becoming functional cells. When one V-J rearrangement is over-represented in a population of B or T cells indicating an origin from a single cell, this indicates a clonal process. Clonality aids in the diagnosis and monitoring of lymphoproliferative disorders and evaluation of disease recurrence. This study aimed to develop objective criteria, which can be automated, to classify B and T cell clonality results as positive (clonal), No evidence of clonality, or invalid (failed). Using clinical samples with "gold standard" clonality data obtained using PCR/CE testing, we ran NGS-based amplicon clonality assays and developed our own model for clonality reporting. To assess the performance of our model, we analyzed the NGS results across other published models. Our model for clonality calling using NGS-based technology increases the assay's sensitivity, more accurately detecting clonality. In addition, we have built a computational pipeline to use our model to objectively call clonality in an automated fashion. Collectively the results outlined below will have a direct clinical impact by expediting the review and sign-out process for concise clonality reporting.

Spatiotemporal assessment of immunogenomic heterogeneity in multiple myeloma.

Spatial heterogeneity is a common phenomenon in metastatic solid tumors and an evolving concept in multiple myeloma (MM). The interplay between malignant plasma cells (PCs) and the microenvironment has not yet been analyzed in MM. For this purpose, we performed bone marrow aspirates and imaging-guided biopsies of corresponding lesions in newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) patients. PCs were isolated and subjected to whole-exome sequencing (WES). Non-PCs were studied with next-generation flow (NGF) and T-cell receptor sequencing (TCRseq) to analyze the connection between malignant and nonmalignant cells in the bone marrow and in lesions. Although we observed a strong overlap from WES, NGF, and TCRseq in patients with intramedullary disease, WES revealed significant spatial heterogeneity in patients with extramedullary disease. NGF showed significant immunosuppression in RRMM compared with NDMM as indicated by fewer myeloid dendritic cells, unswitched memory B cells, Th9 cells, and CD8 effector memory T cells but more natural killer and regulatory T cells. Additionally, fewer T-cell receptor (TCR) sequences were detected in RRMM compared with NDMM and healthy individuals. After induction therapy, TCR repertoire richness increased to levels of healthy individuals, and NGF showed more regulatory T cells and myeloid-derived suppressor cells, regardless of depth of response. Clinical significance of imaging-guided biopsies of lesions was demonstrated by detection of monoclonal PCs in patients without measurable residual disease (MRD) in aspirates from the iliac crest as well as identification of secondary primary malignancies in MRD- patients. Furthermore, site-specific clones with different drug susceptibilities and genetically defined high-risk features were detected by our workflow.

Deciphering spatial genomic heterogeneity at a single cell resolution in multiple myeloma.

Osteolytic lesions (OL) characterize symptomatic multiple myeloma. The mechanisms of how malignant plasma cells (PC) cause OL in one region while others show no signs of bone destruction despite subtotal infiltration remain unknown. We report on a single-cell RNA sequencing (scRNA-seq) study of PC obtained prospectively from random bone marrow aspirates (BM) and paired imaging-guided biopsies of OL. We analyze 148,630 PC from 24 different locations in 10 patients and observe vast inter- and intra-patient heterogeneity based on scRNA-seq analyses. Beyond the limited evidence for spatial heterogeneity from whole-exome sequencing, we find an additional layer of complexity by integrated analysis of anchored scRNA-seq datasets from the BM and OL. PC from OL are characterized by differentially expressed genes compared to PC from BM, including upregulation of genes associated with myeloma bone disease like DKK1, HGF and TIMP-1 as well as recurrent downregulation of JUN/FOS, DUSP1 and HBB. Assessment of PC from longitudinally collected samples reveals transcriptional changes after induction therapy. Our study contributes to the understanding of destructive myeloma bone disease.

Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast.

BACKGROUND: A major challenge in the interpretation of genomic profiling data generated from breast cancer samples is the identification of driver genes as distinct from bystander genes which do not impact tumorigenesis. One way to assess the relative importance of alterations in the transcriptome profile is to combine parallel analyses that assess changes in the copy number alterations (CNAs). This integrated analysis permits the identification of genes with altered expression that map within specific chromosomal regions which demonstrate copy number alterations, providing a mechanistic approach to identify the 'driver genes'. METHODS: We have performed whole genome analysis of CNAs using the Affymetrix 250K Mapping array on 22 infiltrating ductal carcinoma samples (IDCs). Analysis of transcript expression alterations was performed using the Affymetrix U133 Plus2.0 array on 16 IDC samples. Fourteen IDC samples were analyzed using both platforms and the data integrated. We also incorporated data from loss of heterozygosity (LOH) analysis to identify genes showing altered expression in LOH regions. RESULTS: Common chromosome gains and amplifications were identified at 1q21.3, 6p21.3, 7p11.2-p12.1, 8q21.11 and 8q24.3. A novel amplicon was identified at 5p15.33. Frequent losses were found at 1p36.22, 8q23.3, 11p13, 11q23, and 22q13. Over 130 genes were identified with concurrent increases or decreases in expression that mapped to these regions of copy number alterations. LOH analysis revealed three tumors with whole chromosome or p arm allelic loss of chromosome 17. Genes were identified that mapped to copy neutral LOH regions. LOH with accompanying copy loss was detected on Xp24 and Xp25 and genes mapping to these regions with decreased expression were identified. Gene expression data highlighted the PPARα/RXRα Activation Pathway as down-regulated in the tumor samples. CONCLUSION: We have demonstrated the utility of the application of integrated analysis using high resolution CGH and whole genome transcript analysis for detecting driver genes in IDC. The high resolution platform allowed a refined demarcation of CNAs and gene expression profiling provided a mechanism to detect genes directly impacted by the CNA. This is the first report of LOH integrated with gene expression in IDC using a high resolution platform.

Interpreting aCGH-defined karyotypic changes in gliomas using copy number status, loss of heterozygosity and allelic ratios.

We have used SNP mapping arrays to simultaneously record copy number changes, loss of heterozygosity and allele ratios (ploidy) in a series of 13 gliomas. This combined analysis has defined novel amplification events in this tumor type involving chr1:241544532-243005121 and chr18:54716681-54917277 which contain the AKT3 and ZNF532 genes, respectively. The high resolution of this analysis has also identified homozygous deletions involving chr17:25600031-26490848 and Chr19:53883612-55061878. Throughout the karyotypes of these tumors, the combined analysis revealed counter intuitive relationships between copy number and LOH that requires reinterpretation of the significance of copy number gains and losses. It was not uncommon to observe copy number gains that were associated with loss of heterozygosity as well as copy number losses that were not. These events appeared to be related to ploidy status in the tumors as determined using allelic ratio calculations. Overall, this analysis of gliomas provides evidence for the need to perform more comprehensive interpretation of the CGH data beyond copy number analysis alone to evaluate the significance of individual events in the karyotypes.

Copy number and gene expression alterations in radiation-induced papillary thyroid carcinoma from chernobyl pediatric patients.

BACKGROUND: Following exposure to radiation during the Chernobyl fallout tragedy, papillary thyroid carcinoma (PTC) increased significantly in individuals who were children at the time of the accident. We have used two high-throughput, whole genome platforms to analyze radiation-induced PTCs from pediatric patients from the Chernobyl region. METHODS: We performed comparative genomic hybridization using Affymetrix 50K Mapping arrays and gene expression profiling on 10 pediatric post-Chernobyl PTCs obtained from patients living in the region. We performed an overlay analysis of these two data sets. RESULTS: Many regions of copy number alterations (CNAs) were detected including novel regions that had never been associated with PTCs. Increases in copy numbers were consistently found on chromosomes 1p, 5p, 9q, 12q, 13q, 16p, 21q, and 22q. Deletions were observed less frequently and were mapped to 1q, 6q, 9q, 10q, 13q, 14q, 21q, and 22q. Gene expression analysis revealed that most of the altered genes were also perturbed in sporadic adult PTC; however, 141 gene expression changes were found to be unique to the post-Chernobyl tumors. The genes with the highest increases in expression that were novel to the pediatric post-Chernobyl tumors were TESC, PDZRN4, TRAa/TRDa, GABBR2, and CA12. The genes showing the largest expression decreases included PAPSS2, PDLIM3, BEXI, ANK2, SORBS2, and PPARGCIA. An overlay analysis of the gene expression and CNA profiles was then performed. This analysis identified genes showing both CNAs and concurrent gene expression alterations. Many of these are commonly seen in sporadic PTC such as SERPINA, COL8A, and PDX, while others were unique to the radiation-induced profiles including CAMK2N1, AK1, DHRS3, and PDE9A. CONCLUSIONS: This type of analysis allows an assessment of gene expression changes that are associated with a physical mechanism. These genes and chromosomal regions are potential markers for radiation-induced PTC.