Amie Radenbaugh

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing.

Significance Tumor cells are heterogeneous, and much variation occurs at the single-cell level, which may contribute to therapeutic response. Here, we studied drug resistance dynamics in a model of tolerance with a metastatic breast cancer cell line by leveraging the power of single-cell RNA-Seq technology. Drug-tolerant cells within a single clone rapidly express high cell-to-cell transcript variability, with a gene expression profile similar to untreated cells, and the population reacquires paclitaxel sensitivity. Our gene expression and single nucleotide variants analyses suggest that equivalent phenotypes are achieved without relying on a unique molecular event or fixed transcriptional programs. Thus, transcriptional heterogeneity might ensure survival of cancer cells with equivalent combinations of gene expression programs and/or single nucleotide variants. The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drug-tolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

RADIA: RNA and DNA integrated analysis for somatic mutation detection

The detection of somatic single nucleotide variants is a crucial component to the characterization of the cancer genome. Mutation calling algorithms thus far have focused on comparing the normal and tumor genomes from the same individual. In recent years, it has become routine for projects like The Cancer Genome Atlas (TCGA) to also sequence the tumor RNA. Here we present RADIA (RNA and DNA Integrated Analysis), a novel computational method combining the patient-matched normal and tumor DNA with the tumor RNA to detect somatic mutations. The inclusion of the RNA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating an individual’s DNA and RNA, we are able to detect mutations that would otherwise be missed by traditional algorithms that examine only the DNA. We demonstrate high sensitivity (84%) and very high precision (98% and 99%) for RADIA in patient data from endometrial carcinoma and lung adenocarcinoma from TCGA. Mutations with both high DNA and RNA read support have the highest validation rate of over 99%. We also introduce a simulation package that spikes in artificial mutations to patient data, rather than simulating sequencing data from a reference genome. We evaluate sensitivity on the simulation data and demonstrate our ability to rescue back mutations at low DNA allelic frequencies by including the RNA. Finally, we highlight mutations in important cancer genes that were rescued due to the incorporation of the RNA.

Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

About 16% of breast cancers fall into a clinically aggressive category designated triple negative (TNBC) due to a lack of ERBB2, estrogen receptor and progesterone receptor expression1-3. The mutational spectrum of TNBC has been characterized as part of The Cancer Genome Atlas (TCGA)4; however, snapshots of primary tumors cannot reveal the mechanisms by which TNBCs progress and spread. To address this limitation we initiated the Intensive Trial of OMics in Cancer (ITOMIC)-001, in which patients with metastatic TNBC undergo multiple biopsies over space and time5. Whole exome sequencing (WES) of 67 samples from 11 patients identified 426 genes containing multiple distinct single nucleotide variants (SNVs) within the same sample, instances we term Multiple SNVs affecting the Same Gene and Sample (MSSGS). We find that >90% of MSSGS result from cis-compound mutations (in which both SNVs affect the same allele), that MSSGS comprised of SNVs affecting adjacent nucleotides arise from single mutational events, and that most other MSSGS result from the sequential acquisition of SNVs. Some MSSGS drive cancer progression, as exemplified by a TNBC driven by FGFR2(S252W;Y375C). MSSGS are more prevalent in TNBC than other breast cancer subtypes and occur at higher-than-expected frequencies across TNBC samples within TCGA. MSSGS may denote genes that play as yet unrecognized roles in cancer progression.

Abstract 59: Identification of RNA editing events in cancer using high-throughput sequencing data

RNA editing is a post-transcriptional modification of pre-mRNA that has recently been identified as an additional epigenetic mechanism relevant to cancer development and progression. With projects like The Cancer Genome Atlas (TCGA) providing high-throughput sequencing datasets measuring both DNA and RNA from the same patients across multiple cancers, it is now possible to search for RNA editing events at a genome-wide scale. Using fully-sequenced tumor and matched-normal genomes and RNA-Seq data from TCGA project, we will identify RNA editing events in acute myeloid leukemia (AML) patients. We have analyzed the tumor and matched normal genomes to identify SNPs, mutations (both germline and somatic), and heterozygosity across the entire genome. By comparing the patient9s genomic data to the RNA transcripts assembled by the UCSC RNA-Seq pipeline, we can identify any bases that were transcribed abnormally. All putative RNA editing events will be assessed according to the most common types of RNA editing, such as the deamination of adenosine into inosine (A-to-I) or the conversion of cytosine into uracil (C-to-U). Local phasing information inferred from the genomic sequence will be used to disambiguate potential RNA editing events found at heterozygous locations. As a positive control, we will confirm RNA-editing events previously discovered experimentally in AML patients, such as an A-to-I conversion in the protein tyrosine phosphatase PTPN6 gene. The PTPN6 gene is recognized as a tumor suppressor gene and is important for the down-regulation of growth-promoting receptors. The A-to-I conversion of adenosine 7866 causes the splicing mechanism to ignore a splicing junction, leading to a non-functional PTPN6 protein via the inclusion of an intron in the mature RNA transcript. Using the TCGA sequencing data, we will identify all AML samples that exhibit this particular A-to-I conversion as well as the inclusion of the intron with the RNA-Seq data. In addition, we will report novel RNA editing events in AML and other cancer types and look for patterns that may be cancer specific or globally relevant to cancer development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 59. doi:10.1158/1538-7445.AM2011-59