Jennifer M. Carr

Integrated Analysis of Gene Expression, CpG Island Methylation, and Gene Copy Number in Breast Cancer Cells by Deep Sequencing

We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+) and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A), and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER− cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER− cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5′ end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER− breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

Multi-Platform Analysis of MicroRNA Expression Measurements in RNA from Fresh Frozen and FFPE Tissues

MicroRNAs play a role in regulating diverse biological processes and have considerable utility as molecular markers for diagnosis and monitoring of human disease. Several technologies are available commercially for measuring microRNA expression. However, cross-platform comparisons do not necessarily correlate well, making it difficult to determine which platform most closely represents the true microRNA expression level in a tissue. To address this issue, we have analyzed RNA derived from cell lines, as well as fresh frozen and formalin-fixed paraffin embedded tissues, using Affymetrix, Agilent, and Illumina microRNA arrays, NanoString counting, and Illumina Next Generation Sequencing. We compared the performance within- and between the different platforms, and then verified these results with those of quantitative PCR data. Our results demonstrate that the within-platform reproducibility for each method is consistently high and although the gene expression profiles from each platform show unique traits, comparison of genes that were commonly detectable showed that detection of microRNA transcripts was similar across multiple platforms.

Differential Expression of MicroRNAs in Tumors from Chronically Inflamed or Genetic (APCMin/+) Models of Colon Cancer

Background Chronic inflammation associated with ulcerative colitis predisposes individuals to increased colon cancer risk. The aim of these studies was to identify microRNAs that are aberrantly regulated during inflammation and may participate in transformation of colonic epithelial cells in the inflammatory setting. Methodology/Principal Findings We have use quantitative PCR arrays to compare microRNA (miRNA) expression in tumors and control colonic epithelial cells isolated from distal colons of chronically inflamed mice and APCMin/+ mice. Rank order statistics was utilized to identify differentially regulated miRNAs in tumors that arose due to chronic inflammation and/or to germline APC mutation. Eight high priority miRNAs were identified: miR-215, miR-137, miR-708, miR-31, and miR-135b were differentially expressed in APC tumors and miR-215, miR-133a, miR-467d, miR-218, miR-708, miR-31, and miR-135b in colitis-associated tumors. Four of these (miR-215, miR-708, miR-31, and miR-135b) were common to both tumors types, and dysregulation of these miRNAs was confirmed in an independent sample set. Target prediction and pathway analysis suggests that these microRNAs, in the aggregate, regulate signaling pathways related to MAPK, PI3K, WNT, and TGF-β, all of which are known to be involved in transformation. Conclusions/Significance We conclude that these four miRNAs are dysregulated at some very early stage in transformation of colonic epithelial cells. This response is not dependent on the mechanism of initiation of transformation (inflammation versus germline mutation), suggesting that the miRNAs that we have identified are likely to regulate critical signaling pathways that are central to early events in transformation of colonic epithelial cells.

Deep Sequence Analysis of Non-Small Cell Lung Cancer: Integrated Analysis of Gene Expression, Alternative Splicing, and Single Nucleotide Variations in Lung Adenocarcinomas with and without Oncogenic KRAS Mutations

KRAS mutations are highly prevalent in non-small cell lung cancer (NSCLC), and tumors harboring these mutations tend to be aggressive and resistant to chemotherapy. We used next-generation sequencing technology to identify pathways that are specifically altered in lung tumors harboring a KRAS mutation. Paired-end RNA-sequencing of 15 primary lung adenocarcinoma tumors (8 harboring mutant KRAS and 7 with wild-type KRAS) were performed. Sequences were mapped to the human genome, and genomic features, including differentially expressed genes, alternate splicing isoforms and single nucleotide variants, were determined for tumors with and without KRAS mutation using a variety of computational methods. Network analysis was carried out on genes showing differential expression (374 genes), alternate splicing (259 genes), and SNV-related changes (65 genes) in NSCLC tumors harboring a KRAS mutation. Genes exhibiting two or more connections from the lung adenocarcinoma network were used to carry out integrated pathway analysis. The most significant signaling pathways identified through this analysis were the NFκB, ERK1/2, and AKT pathways. A 27 gene mutant KRAS-specific sub network was extracted based on gene–gene connections from the integrated network, and interrogated for druggable targets. Our results confirm previous evidence that mutant KRAS tumors exhibit activated NFκB, ERK1/2, and AKT pathways and may be preferentially sensitive to target therapeutics toward these pathways. In addition, our analysis indicates novel, previously unappreciated links between mutant KRAS and the TNFR and PPARγ signaling pathways, suggesting that targeted PPARγ antagonists and TNFR inhibitors may be useful therapeutic strategies for treatment of mutant KRAS lung tumors. Our study is the first to integrate genomic features from RNA-Seq data from NSCLC and to define a first draft genomic landscape model that is unique to tumors with oncogenic KRAS mutations.

Gene expression, single nucleotide variant and fusion transcript discovery in archival material from breast tumors.

Advantages of RNA-Seq over array based platforms are quantitative gene expression and discovery of expressed single nucleotide variants (eSNVs) and fusion transcripts from a single platform, but the sensitivity for each of these characteristics is unknown. We measured gene expression in a set of manually degraded RNAs, nine pairs of matched fresh-frozen, and FFPE RNA isolated from breast tumor with the hybridization based, NanoString nCounter (226 gene panel) and with whole transcriptome RNA-Seq using RiboZeroGold ScriptSeq V2 library preparation kits. We performed correlation analyses of gene expression between samples and across platforms. We then specifically assessed whole transcriptome expression of lincRNA and discovery of eSNVs and fusion transcripts in the FFPE RNA-Seq data. For gene expression in the manually degraded samples, we observed Pearson correlations of >0.94 and >0.80 with NanoString and ScriptSeq protocols, respectively. Gene expression data for matched fresh-frozen and FFPE samples yielded mean Pearson correlations of 0.874 and 0.783 for NanoString (226 genes) and ScriptSeq whole transcriptome protocols respectively, p<2x10-16. Specifically for lincRNAs, we observed superb Pearson correlation (0.988) between matched fresh-frozen and FFPE pairs. FFPE samples across NanoString and RNA-Seq platforms gave a mean Pearson correlation of 0.838. In FFPE libraries, we detected 53.4% of high confidence SNVs and 24% of high confidence fusion transcripts. Sensitivity of fusion transcript detection was not overcome by an increase in depth of sequencing up to 3-fold (increase from ~56 to ~159 million reads). Both NanoString and ScriptSeq RNA-Seq technologies yield reliable gene expression data for degraded and FFPE material. The high degree of correlation between NanoString and RNA-Seq platforms suggests discovery based whole transcriptome studies from FFPE material will produce reliable expression data. The RiboZeroGold ScriptSeq protocol performed particularly well for lincRNA expression from FFPE libraries, but detection of eSNV and fusion transcripts was less sensitive.

RNA Sequencing Identifies Multiple Fusion Transcripts, Differentially Expressed Genes, and Reduced Expression of Immune Function Genes in BRAF (V600E) Mutant vs BRAF Wild-Type Papillary Thyroid Carcinoma

CONTEXT The BRAF V600E mutation (BRAF-MUT) confers an aggressive phenotype in papillary thyroid carcinoma, but unidentified additional genomic abnormalities may be required for full phenotypic expression. OBJECTIVE RNA sequencing (RNA-Seq) was performed to identify genes differentially expressed between BRAF-MUT and BRAF wild-type (BRAF-WT) tumors and to correlate changes to patient clinical status. DESIGN BRAF-MUT and BRAF-WT tumors were identified in patients with T1N0 and T2-3N1 tumors evaluated in a referral medical center. Gene expression levels were determined (RNA-Seq) and fusion transcripts were detected. Multiplexed capture/detection and digital counting of mRNA transcripts (nCounter, NanoString Technologies) validated RNA-Seq data for immune system-related genes. PATIENTS BRAF-MUT patients included nine women, three men; nine were TNM stage I and three were stage III. Three (25%) had tumor infiltrating lymphocytes. BRAF-WT included five women, three men; all were stage I, and five (62.5%) had tumor infiltrating lymphocytes. RESULTS RNA-Seq identified 560 of 13 085 genes differentially expressed between BRAF-MUT and BRAF-WT tumors. Approximately 10% of these genes were related to MetaCore immune function pathways; 51 were underexpressed in BRAF-MUT tumors, whereas 4 (HLAG, CXCL14, TIMP1, IL1RAP) were overexpressed. The four most differentially overexpressed immune genes in BRAF-WT tumors (IL1B; CCL19; CCL21; CXCR4) correlated with lymphocyte infiltration. nCounter confirmed the RNA-Seq expression level data. Eleven different high-confidence fusion transcripts were detected (four interchromosomal; seven intrachromosomal) in 13 of 20 tumors. All in-frame fusions were validated by RT-PCR. CONCLUSION BRAF-MUT papillary thyroid cancers have reduced expression of immune/inflammatory response genes compared with BRAF-WT tumors and correlate with lymphocyte infiltration. In contrast, HLA-G and CXCL14 are overexpressed in BRAF-MUT tumors. Sixty-five percent of tumors had between one and three fusion transcripts. Functional studies will be required to determine the potential role of these newly identified genomic abnormalities in contributing to the aggressiveness of BRAF-MUT and BRAF-WT tumors.

AT-101 downregulates BCL2 and MCL1 and potentiates the cytotoxic effects of lenalidomide and dexamethasone in preclinical models of multiple myeloma and Waldenström macroglobulinaemia.

Multiple myeloma, the second most common haematological malignancy in the U.S., is currently incurable. Disruption of the intrinsic apoptotic pathway by BCL2 and MCL1 upregulation is observed in >80% of myeloma cases and is associated with an aggressive clinical course. Remarkably, there is no approved drug with the ability to target BCL2 or MCL1. Thus, we investigated the anti‐tumour effects of a pan‐BCL2 inhibitor, AT‐101, which has high binding specificity for BCL2 and MCL1 in preclinical models of plasma cell cancers (Multiple myeloma and Waldenström macroglobulinaemia). Gene expression and immunoblot analysis of six plasma cell cancer models showed upregulation of BCL2 family members. AT‐101 was able to downregulate BCL2 and MCL1 in all plasma cell cancer models and induced apoptotic cell death in a caspase‐dependent manner by altering mitochondrial membrane permeability. This cytotoxic effect and BCL2 downregulation were further potentiated when AT‐101 was combined with lenalidomide/dexamethasone (LDA). NanoString nCounter mRNA quantification and Ingenuity Pathways Analysis revealed differential changes in the CCNA2, FRZB, FYN, IRF1, PTPN11 genes in LDA‐treated cells. In summary, we describe for the first time the cellular and molecular events associated with the use of AT‐101 in combination with lenalidomide/dexamethasone in preclinical models of plasma cell malignancy.

Intrinsic Subtype and Therapeutic Response Among HER2-Positive Breast Tumors from the NCCTG (Alliance) N9831 Trial

Background: Genomic data from human epidermal growth factor receptor 2‐positive (HER2+) tumors were analyzed to assess the association between intrinsic subtype and clinical outcome in a large, well‐annotated patient cohort. Methods: Samples from the NCCTG (Alliance) N9831 trial were analyzed using the Prosigna algorithm on the NanoString platform to define intrinsic subtype, risk of recurrence scores, and risk categories for 1392 HER2+ tumors. Subtypes were evaluated for recurrence‐free survival (RFS) using Kaplan‐Meier and Cox model analysis following adjuvant chemotherapy (n = 484) or chemotherapy plus trastuzumab (n = 908). All statistical tests were two‐sided. Results: Patients with HER2+ tumors from N9831 were primarily scored as HER2‐enriched (72.1%). These individuals received statistically significant benefit from trastuzumab (hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.52 to 0.89, P = .005), as did the patients (291 of 1392) with luminal‐type tumors (HR = 0.52, 95% CI = 0.32 to 0.85, P = .01). Patients with basal‐like tumors (97 of 1392) did not have statistically significantly better RFS when treated with trastuzumab and chemotherapy compared with chemotherapy alone (HR = 1.06, 95% CI = 0.53 to 2.13, P = .87). Conclusions: The majority of clinically defined HER2‐positive tumors were classified as HER2‐enriched or luminal using the Prosigna algorithm. Intrinsic subtype alone cannot replace conventional histopathological evaluation of HER2 status because many tumors that are classified as luminal A or luminal B will benefit from adjuvant trastuzumab if that subtype is accompanied by HER2 overexpression. However, among tumors that overexpress HER2, we speculate that assessment of intrinsic subtype may influence treatment, particularly with respect to evaluating alternative therapeutic approaches for that subset of HER2‐positive tumors of the basal‐like subtype.

An integrated model of the transcriptome of HER2-positive breast cancer.

Our goal in these analyses was to use genomic features from a test set of primary breast tumors to build an integrated transcriptome landscape model that makes relevant hypothetical predictions about the biological and/or clinical behavior of HER2-positive breast cancer. We interrogated RNA-Seq data from benign breast lesions, ER+, triple negative, and HER2-positive tumors to identify 685 differentially expressed genes, 102 alternatively spliced genes, and 303 genes that expressed single nucleotide sequence variants (eSNVs) that were associated with the HER2-positive tumors in our survey panel. These features were integrated into a transcriptome landscape model that identified 12 highly interconnected genomic modules, each of which represents a cellular processes pathway that appears to define the genomic architecture of the HER2-positive tumors in our test set. The generality of the model was confirmed by the observation that several key pathways were enriched in HER2-positive TCGA breast tumors. The ability of this model to make relevant predictions about the biology of breast cancer cells was established by the observation that integrin signaling was linked to lapatinib sensitivity in vitro and strongly associated with risk of relapse in the NCCTG N9831 adjuvant trastuzumab clinical trial dataset. Additional modules from the HER2 transcriptome model, including ubiquitin-mediated proteolysis, TGF-beta signaling, RHO-family GTPase signaling, and M-phase progression, were linked to response to lapatinib and paclitaxel in vitro and/or risk of relapse in the N9831 dataset. These data indicate that an integrated transcriptome landscape model derived from a test set of HER2-positive breast tumors has potential for predicting outcome and for identifying novel potential therapeutic strategies for this breast cancer subtype.

Impact of library preparation on downstream analysis and interpretation of RNA-Seq data: comparison between Illumina PolyA and NuGEN Ovation protocol.

Objectives The sequencing by the PolyA selection is the most common approach for library preparation. With limited amount or degraded RNA, alternative protocols such as the NuGEN have been developed. However, it is not yet clear how the different library preparations affect the downstream analyses of the broad applications of RNA sequencing. Methods and Materials Eight human mammary epithelial cell (HMEC) lines with high quality RNA were sequenced by Illumina’s mRNA-Seq PolyA selection and NuGEN ENCORE library preparation. The following analyses and comparisons were conducted: 1) the numbers of genes captured by each protocol; 2) the impact of protocols on differentially expressed gene detection between biological replicates; 3) expressed single nucleotide variant (SNV) detection; 4) non-coding RNAs, particularly lincRNA detection; and 5) intragenic gene expression. Results Sequences from the NuGEN protocol had lower (75%) alignment rate than the PolyA (over 90%). The NuGEN protocol detected fewer genes (12–20% less) with a significant portion of reads mapped to non-coding regions. A large number of genes were differentially detected between the two protocols. About 17–20% of the differentially expressed genes between biological replicates were commonly detected between the two protocols. Significantly higher numbers of SNVs (5–6 times) were detected in the NuGEN samples, which were largely from intragenic and intergenic regions. The NuGEN captured fewer exons (25% less) and had higher base level coverage variance. While 6.3% of reads were mapped to intragenic regions in the PolyA samples, the percentages were much higher (20–25%) for the NuGEN samples. The NuGEN protocol did not detect more known non-coding RNAs such as lincRNAs, but targeted small and “novel” lincRNAs. Conclusion Different library preparations can have significant impacts on downstream analysis and interpretation of RNA-seq data. The NuGEN provides an alternative for limited or degraded RNA but it has limitations for some RNA-seq applications.