A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

BackgroundOncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.ResultsIn reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100x more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.ConclusionThese new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.Peer reviewe

Genetic improvement of tomato by targeted control of fruit softening

Controlling the rate of softening to extend shelf life was a key target for researchers engineering genetically modified (GM) tomatoes in the 1990s, but only modest improvements were achieved. Hybrids grown nowadays contain 'non-ripening mutations' that slow ripening and improve shelf life, but adversely affect flavor and color. We report substantial, targeted control of tomato softening, without affecting other aspects of ripening, by silencing a gene encoding a pectate lyase

Differentially expressed genes (DEGs) identified from RNA-Seq by DESeq and Cuffdiff compared to microarray.

<p>Panel A. Number of DEGs observed with change ≥2-fold at p≤0.005 by DESeq, microarray and Cuffdiff analyses with Cufflinks assembled transcripts for all eight animals (4 Control, 4 AFB1) is shown. Total number of Cufflinks assembled transcripts and those that match an existing RefSeq annotation are displayed in the bar chart. Panel B. Venn diagram of DEGs from Panel A shows the number of common transcripts (overlapping circles) and unique transcripts (non-overlapping circles) for all three analyses. Panel C. Principal component (PC) analysis was performed for all samples using the gene expression values for DEGs found by microarray and DESeq (on Cufflinks assembled transcripts) analysis. The percentage variability captured by the first three principal components is displayed across PC#1, 2 and 3 represented on X, Y and Z axes. Panel D. Heatmap shows all DEGs at ≥2-fold change, p≤0.005 from microarray or DESeq analyses (on Cufflinks assembled transcripts). Gene expression data were log2 transformed and then quantile normalized prior to generating the Heatmap for direct comparison of data. DEGs (red or green are upregulated or downregulated, respectively) for each animal were mapped by lane for each of four animals in the CNTL or AFB1 treatment groups.</p

RNA-Seq Profiling Reveals Novel Hepatic Gene Expression Pattern in Aflatoxin B1 Treated Rats

<div><p>Deep sequencing was used to investigate the subchronic effects of 1 ppm aflatoxin B1 (AFB1), a potent hepatocarcinogen, on the male rat liver transcriptome prior to onset of histopathological lesions or tumors. We hypothesized RNA-Seq would reveal more differentially expressed genes (DEG) than microarray analysis, including low copy and novel transcripts related to AFB1’s carcinogenic activity compared to feed controls (CTRL). Paired-end reads were mapped to the rat genome (Rn4) with TopHat and further analyzed by DESeq and Cufflinks-Cuffdiff pipelines to identify differentially expressed transcripts, new exons and unannotated transcripts. PCA and cluster analysis of DEGs showed clear separation between AFB1 and CTRL treatments and concordance among group replicates. qPCR of eight high and medium DEGs and three low DEGs showed good comparability among RNA-Seq and microarray transcripts. DESeq analysis identified 1,026 differentially expressed transcripts at greater than two-fold change (p<0.005) compared to 626 transcripts by microarray due to base pair resolution of transcripts by RNA-Seq, probe placement within transcripts or an absence of probes to detect novel transcripts, splice variants and exons. Pathway analysis among DEGs revealed signaling of Ahr, Nrf2, GSH, xenobiotic, cell cycle, extracellular matrix, and cell differentiation networks consistent with pathways leading to AFB1 carcinogenesis, including almost 200 upregulated transcripts controlled by E2f1-related pathways related to kinetochore structure, mitotic spindle assembly and tissue remodeling. We report 49 novel, differentially-expressed transcripts including confirmation by PCR-cloning of two unique, unannotated, hepatic AFB1-responsive transcripts (HAfT’s) on chromosomes 1.q55 and 15.q11, overexpressed by 10 to 25-fold. Several potentially novel exons were found and exon refinements were made including AFB1 exon-specific induction of homologous family members, Ugt1a6 and Ugt1a7c. We find the rat transcriptome contains many previously unidentified, AFB1-responsive exons and transcripts supporting RNA-Seq’s capabilities to provide new insights into AFB1-mediated gene expression leading to hepatocellular carcinoma.</p></div

Novel exons found by RNA-Seq analysis.

<p>Panel A shows classification of different types of exons encountered during analysis of a Cufflinks assembled transcript in a model gene. Exons include ‘Exact’ matches to known exons, ‘Overlapping’ exons corresponding to partial matches, ‘Novel-T’ exons that occur with known transcripts, ‘Within’ exons occurring within the sequence of known exons and Novel-U exons that were unknown and occur outside known transcripts. Panel B is a bar chart of such exon types that were unique to AFB1 or Control treatments and those exons that were shared between treatments. Examples are shown for a Novel-T exon (Panel C) within the F11 transcript and two Novel-U exons (Panel D) outside the ASS1 gene. Numbers of reads, as RPM, are shown on the Y-axis and the genomic region is displayed on the X-axis for representative AFB1 (blue reads) and CTRL (black reads) sample tracks in UCSC browser format.</p

Exon specific expression among homologous transcripts in the Ugt1a gene family.

<p>Panel A. The genomic region for Ugt1a transcripts is displayed on the X-axis in UCSC browser format where the Y-axis represents mapped reads in RPM units. Placements of microarray probes for specific transcripts are indicated by rose-colored boxes with probe names below. There are a total of four microarray probes, some of which correspond to shared exons of the Ugta1 gene family (A_44_P432355, A_44_P402641) or to specific exons defining Ugt1a1 (A_44_P446578) and Ugt1a6 (A_44_P432358) isoforms. RefSeq transcripts and Cufflinks assembled transcripts are displayed under the RNA-Seq tracks. Exons are shown as light blue blocks or bands; introns are lines between exons; arrows at the end of each transcript indicate direction of transcription. Panel B. Bar graph shows mean fold changes (AFB1/Control) on the Y axis for the entire RNA-Seq transcript (blue), the microarray probe (red) and the isoform-specific RNA-Seq_exon (green). For some exons, there was no corresponding microarray probe − ‘No Probe’ (e.g. Ugt1a5, Ugta1a7c). Exon-specific, RNA-Seq ratios were labeled by exon number. Ugt1a-Common consists of four exons (common to all Ugt1a isoforms) for which two microarray probes exist. Exon-specific ratios from RNA-Seq reads were calculated for Exons 1, 2, 3 and 4. RNA-Seq exon-specific reads were measured to calculate AFB1/Control ratios for Ugt1a1, Utgt1a5, both exons of Ugt1a6, and Ugt1a7c.</p

Analysis for Differentially Expressed Genes (DEGs)^a.

a<p>Transcripts were grouped by various combinations of analysis into three columns: Total Assembled transcripts; RefSeq - transcripts matching or partially matching RefSeq genes; and Novel transcripts. In the first row, Total CuffCompare transcripts included all transcripts (57,076); RefSeq transcripts (complete or partial match) were 45,144 (14,257+30,887 = 45,144); and 11,932 potentially novel transcripts. Note, that a set of 1,496 Cufflinks assembled transcripts referred to as, ‘Others’, contained significant repeat sequences and so this small set of transcripts was excluded (e.g. –‘Others’) from Total Assembled transcripts. In row 2, to enable comparison with available Microarray data, we identified Cufflinks assembled transcripts that overlapped MA probes (total 44,469 transcripts). From this group of transcripts, we determined differential expression (DEGs) using student’s t-test in the case of MA (microarray) data in row 4, or for RNA-Seq data we performed DESeq analysis in row 3 or Cuffdiff analysis in row 5 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061768#s4" target="_blank">Materials and Methods</a> for details).</p

E2f1 regulated and downstream pathways altered by AFB1.

<p>AFB1 produced DEGs from DESeq analysis of RNA-Seq data which were analyzed by the IPA’s ‘grow pathway’ analysis (Ingenuity Pathway Analysis) which displays annotated regulatory relationships and interactions. Starting with induction of E2f1 in the center, DEGs from DESeq analysis were used to connect and grow downstream-dependent genes (red, upregulated; green, down-regulated). Hub genes (bolded, enlarged gene symbols) were defined as those transcripts regulating or interacting with ≥5 transcripts.</p

Connection pathway analysis of DEGs from subchronic AFB1 exposure.

<p>The top panel shows annotated interactions and regulatory relationships using IPA’s (Ingenuity Pathway Analysis) connectivity analysis. The connective pathway maps were generated using DEGs identified by DESeq_RNASeq (top panel) and for DEGs generated from microarray analysis (bottom panel) for only those transcripts with available RefSeq annotation. Hub genes (bolded, enlarged gene symbols) were defined as those transcripts regulating or interacting with ≥5 transcripts (red, upregulated; green, down-regulated).</p

Reference-Guided Assembly of RNA-Seq Reads into Transcripts by Cufflinks^a.

a<p>Cufflinks was employed to assemble reads into transcripts from each animal sample and Cuffcompare was used to obtain a union of transcripts from all 8 animals as summarized in this panel. ‘Total Transcripts Assembled’ was the sum of Complete, Partial and Novel transcripts. ‘Complete Match’ transcripts were identical to RefSeq transcripts (intron chain matches RefSeq gene); transcripts in the ‘Partially Match’ category include transcripts that overlap RefSeq transcripts but do not completely match (these could include potentially novel isoforms of an existing RefSeq transcript); and potentially ‘Novel’ transcripts are those that did not overlap with any RefSeq transcripts at all and may therefore involve intergenic transcripts.</p