Noemi Andor

Genomic Instability in Cancer: Teetering on the Limit of Tolerance

Cancer genomic instability contributes to the phenomenon of intratumoral genetic heterogeneity, provides the genetic diversity required for natural selection, and enables the extensive phenotypic diversity that is frequently observed among patients. Genomic instability has previously been associated with poor prognosis. However, we have evidence that for solid tumors of epithelial origin, extreme levels of genomic instability, where more than 75% of the genome is subject to somatic copy number alterations, are associated with a potentially better prognosis compared with intermediate levels under this threshold. This has been observed in clonal subpopulations of larger size, especially when genomic instability is shared among a limited number of clones. We hypothesize that cancers with extreme levels of genomic instability may be teetering on the brink of a threshold where so much of their genome is adversely altered that cells rarely replicate successfully. Another possibility is that tumors with high levels of genomic instability are more immunogenic than other cancers with a less extensive burden of genetic aberrations. Regardless of the exact mechanism, but hinging on our ability to quantify how a tumors burden of genetic aberrations is distributed among coexisting clones, genomic instability has important therapeutic implications. Herein, we explore the possibility that a high genomic instability could be the basis for a tumors sensitivity to DNA-damaging therapies. We primarily focus on studies of epithelial-derived solid tumors. Cancer Res; 77(9); 2179-85. ©2017 AACR.

Abstract 2387: Pan-cancer analysis of clonal evolution reveals the costs and adaptive benefits of genomic instability

Cancers are a mosaic of clones of varying population sizes. Any single cancer sample encodes a tumor-metagenome, since it represents the aggregate genomes of diverse clones that coexist within the sample. We quantified genomic instability as the fraction of the tumor-metagenome affected by copy number variations (CNVs) and leveraged two tumor mixture separation algorithms, EXPANDS and PyClone, to quantify genetic intra-tumor heterogeneity (ITH) from single cancer samples. We tested the potential of measures of genomic instability and ITH as prognostic biomarkers across 1,165 exome sequenced primary tumors from 12 cancer types at TCGA. Our results suggest that a tradeoff between the costs and adaptive benefits of genomic instability governs differential radiotherapy sensitivity. Between 1 and 18 clones were estimated to coexist per tumor sample at >10% cell frequency (median = 4). Clone number varied considerably within and between cancer types, with melanomas representing the most heterogeneous group. 86% all analyzed tumor samples contained at least 2 clones. Across cancer types, the presence of >2 clones was associated with worse overall survival as compared to tumors in which 75% genomic instability was shared among 75% predicted reduced risk (HR = 0.15, 95% CI: 0.08-0.29). We analyzed the relation between radiotherapy intensity and overall survival among 242 individuals (21%) treated with radiotherapy and found that not all individuals did benefit equally from therapy. In order to achieve the same benefit from therapy, individuals with 25-50% genomic instability required higher therapy intensity (regression slope = 1.83; P = 0.009) than individuals with 50-75% genomic instability (slope = 2.09; P = 0.005). In contrast, individuals with Radiotherapy may be particularly effective against tumors with intermediate CNV burden, by pushing them past the limit of ‘tolerable’ genomic instability. Our results from two independent pan-cancer cohorts suggest that this limit is exceeded when >75% of a tumor9s metagenome is affected by CNVs. This upper limit of tolerable genomic instability may be responsible for the non-linear association we observed between genetic ITH and survival. Leveraging a clone9s distance to the upper limit of tolerable genomic instability may represent a new strategy to optimize therapy intensity. Citation Format: Noemi Andor, Trevor A. Graham, Marnix Jansen, Li C. Xia, Athena Aktipis, Claudia Petritsch, Hanlee P. Ji, Carlo C. Maley. Pan-cancer analysis of clonal evolution reveals the costs and adaptive benefits of genomic instability. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2387.

Joint single cell DNA-Seq and RNA-Seq of gastric cancer reveals subclonal signatures of genomic instability and gene expression

Sequencing the genomes of individual cancer cells provides the highest resolution of intratumoral heterogeneity. To enable high throughput single cell DNA-Seq across thousands of individual cells per sample, we developed a droplet-based, automated partitioning technology for whole genome sequencing. We applied this approach on a set of gastric cancer cell lines and a primary gastric tumor. In parallel, we conducted a separate single cell RNA-Seq analysis on these same cancers and used copy number to compare results. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection – where the percentage of replicating cells per clone is higher than expected – indicating ongoing tumor evolution. Our study demonstrates that joining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology. SIGNIFICANCE We conducted a massively parallel DNA sequencing analysis on a set of gastric cancer cell lines and a primary gastric tumor in combination with a joint single cell RNA-Seq analysis. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection where the percentage of replicating cells per clone is higher than expected indicating ongoing tumor evolution. Our study demonstrates that combining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.

Exploring the spatiotemporal genetic heterogeneity in metastatic lung adenocarcinoma using a nuclei flow-sorting approach: "Genomics of flow-sorted primary-metastatic pairs in lung cancer"

Variable tumor cellularity can limit sensitivity and precision in comparative genomics because differences in tumor content can result in misclassifying truncal mutations as region‐specific private mutations in stroma‐rich regions, especially when studying tissue specimens of mediocre tumor cellularity such as lung adenocarcinomas (LUADs). To address this issue, we refined a nuclei flow‐sorting approach by sorting nuclei based on ploidy and the LUAD lineage marker thyroid transcription factor 1 and applied this method to investigate genome‐wide somatic copy number aberrations (SCNAs) and mutations of 409 cancer genes in 39 tumor populations obtained from 16 primary tumors and 21 matched metastases. This approach increased the mean tumor purity from 54% (range 7–89%) of unsorted material to 92% (range 79–99%) after sorting. Despite this rise in tumor purity, we detected limited genetic heterogeneity between primary tumors and their metastases. In fact, 88% of SCNAs and 80% of mutations were propagated from primary tumors to metastases and low allele frequency mutations accounted for much of the mutational heterogeneity. Even though the presence of SCNAs indicated a history of chromosomal instability (CIN) in all tumors, metastases did not have more SCNAs than primary tumors. Moreover, tumors with biallelic TP53 or ATM mutations had high numbers of SCNAs, yet they were associated with a low interlesional genetic heterogeneity. The results of our study thus provide evidence that most macroevolutionary events occur in primary tumors before metastatic dissemination and advocate for a limited degree of CIN over time and space in this cohort of LUADs. Sampling of primary tumors thus may suffice to detect most mutations and SCNAs. In addition, metastases but not primary tumors had seeded additional metastases in three of four patients; this provides a genomic rational for surgical treatment of such oligometastatic LUADs. Copyright

Lgl1 controls NG2 endocytic pathway to regulate oligodendrocyte differentiation and asymmetric cell division and gliomagenesis

Oligodendrocyte progenitor cells (OPC) undergo asymmetric cell division (ACD) to generate one OPC and one differentiating oligodendrocyte (OL) progeny. Loss of pro-mitotic proteoglycan and OPC marker NG2 in the OL progeny is the earliest immunophenotypic change of unknown mechanism that indicates differentiation commitment. Here, we report that expression of the mouse homolog of Drosophila tumor suppressor Lethal giant larvae 1 (Lgl1) is induced during OL differentiation. Lgl1 conditional knockout OPC progeny retain NG2 and show reduced OL differentiation, while undergoing more symmetric self-renewing divisions at the expense of asymmetric divisions. Moreover, Lgl1 and hemizygous Ink4a/Arf knockouts in OPC synergistically induce gliomagenesis. Time lapse and total internal reflection microscopy reveals a critical role for Lgl1 in NG2 endocytic routing and links aberrant NG2 recycling to failed differentiation. These data establish Lgl1 as a suppressor of gliomagenesis and positive regulator of asymmetric division and differentiation in the healthy and demyelinated murine brain.Oligodendrocyte progenitor cells (OPCs) undergo asymmetric cell division, and disruption of such mechanism can generate oligodendroglioma precursors. Here, Daynac and colleagues show that Lgl1 regulates asymmetric division and differentiation of OPCs by interfering with the endocytosis pathway, and that Lgl1 knockout can lead to gliomagenesis.

SVEngine: an efficient and versatile simulator of genome structural variations with features of cancer clonal evolution

Abstract Background Simulating genome sequence data with variant features facilitates the development and benchmarking of structural variant analysis programs. However, there are only a few data simulators that provide structural variants in silico and even fewer that provide variants with different allelic fraction and haplotypes. Findings We developed SVEngine, an open-source tool to address this need. SVEngine simulates next-generation sequencing data with embedded structural variations. As input, SVEngine takes template haploid sequences (FASTA) and an external variant file, a variant distribution file, and/or a clonal phylogeny tree file (NEWICK) as input. Subsequently, it simulates and outputs sequence contigs (FASTAs), sequence reads (FASTQs), and/or post-alignment files (BAMs). All of the files contain the desired variants, along with BED files containing the ground truth. SVEngines flexible design process enables one to specify size, position, and allelic fraction for deletions, insertions, duplications, inversions, and translocations. Finally, SVEngine simulates sequence data that replicate the characteristics of a sequencing library with mixed sizes of DNA insert molecules. To improve the compute speed, SVEngine is highly parallelized to reduce the simulation time. Conclusions We demonstrated the versatile features of SVEngine and its improved runtime comparisons with other available simulators. SVEngines features include the simulation of locus-specific variant frequency designed to mimic the phylogeny of cancer clonal evolution. We validated SVEngines accuracy by simulating genome-wide structural variants of NA12878 and a heterogeneous cancer genome. Our evaluation included checking various sequencing mapping features such as coverage change, read clipping, insert size shift, and neighboring hanging read pairs for representative variant types. Structural variant callers Lumpy and Manta and tumor heterogeneity estimator THetA2 were able to perform realistically on the simulated data. SVEngine is implemented as a standard Python package and is freely available for academic use .

Abstract 438: High-quality CNV segments from low-coverage whole genome sequencing from FFPE cancer biopsies based on an evaluation of multiple CNV tools

Changes in DNA copy number, i.e., somatic CNVs, are common genetic aberrations in cancers. The effects of CNV include alteration in gene dosage across large segments of the cancer genome affecting the expression of cancer driver genes by amplifications, or cancer suppressor genes by deletions. In addition, CNVs are markers of underlying rearrangements within or between chromosomes and there is increasing evidence supporting a greater role for CNVs in developing and maintaining neoplastic cell population diversity. Copy number aberrations can be estimated from next generation sequencing data, with high sensitivity and genomic resolution by sequencing the whole genome (WGS). For this study, we demonstrated that high quality CNV calls can be extracted in a fast and cost-effective way from low-coverage whole genome sequencing. Novaseq S2 flowcells (Illumina Inc) enables to obtain an average coverage of 3-4x per sample after pooling up to 96 samples per flowcell. We examined three different copy number detection tools (CNVkit, BicSeq, and seqCBS) from paired tumor and normal WGS using microarray data as a reference. Pearson correlations were computed between the reference and CNVs from the WGS in two fashion; i) segment based and ii) gene based. The segment based comparison used sliding window of 100 K bp while gene based comparison used segments at the gene level. We found high correlations between microarray and WGS segments. The highest correlations were obtained by CNVkit, ranging from 0.964 to 0.985 (SD: 0.973 - 0.007) and BicSeq, ranging from 0.963 to 0.986 (SD: 0.975 - 0.008). These results open the prospect of assessing large cancer cohorts of hundreds of samples at a reasonable cost. We are planning to apply this method to a large cohort of Stage III colon cancer patients and determine the clinical relevance of CNVs for survival. Citation Format: HoJoon Lee, Li Charlie Xia, Stephanie Greer, John Bell, Sue M. Grimes, Christina Wood Bouwens, Giwon Shin, Billy TC Lau, Lucas Johnson, Noemi Andor, Kenneth Day, Mickey Miller, Helaman Escobar, Lincoln Nadauld, Hanlee P. Ji, Paul Van Hummelen. High-quality CNV segments from low-coverage whole genome sequencing from FFPE cancer biopsies based on an evaluation of multiple CNV tools [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 438.

Single-cell transcriptome analysis identifies distinct cell types and intercellular niche signaling in a primary gastric organoid model

The diverse cellular milieu of the gastric tissue microenvironment plays a critical role in normal tissue homeostasis and tumor development. However, few cell culture model can recapitulate the tissue microenvironment and intercellular signaling in vitro. Here we applied an air-liquid interface method to culture primary gastric organoids that contains epithelium with endogenous stroma. To characterize the microenvironment and intercellular signaling in this model, we analyzed the transcriptomes of over 5,000 individual cells from primary gastric organoids cultured at different time points. We identified epithelial cells, fibroblasts and macrophages at the early stage of organoid formation, and revealed that macrophages were polarized towards wound healing and tumor promotion. The organoids maintained both epithelial and fibroblast lineages during the course of time, and a subset of cells in both lineages expressed the stem cell marker Lgr5. We identified that Rspo3 was specifically expressed in the fibroblast lineage, providing an endogenous source of the R-spondin to activate Wnt signaling. Our studies demonstrate that air-liquid-interface-derived organoids provide a novel platform to study intercellular signaling and immune response in vitro.

Abstract 4339: Single cell RNA sequencing dissects cellular growth factor dependencies and oncogenic driver effects in an organoid model of gastric cancer

Gastric cancer is a lethal malignancy with few therapeutic options. Gastric tumors rely on complex intercellular signaling “crosstalk” that enables tumor development, metastasis, and therapeutic resistance. To recapitulate the intercellular communications among various cell populations that exist in vivo, we are using a three-dimensional culture system to grow and manipulate mouse gastric tissue in vitro, otherwise referred to as organoids. Importantly, this organoid model contains epithelium with its endogenous mesenchymal niche and does not require exogenous Wnt stimulation. To systematically analyze the distinct cellular lineages and their interactions, we applied a massively-scaled single cell RNA-Seq platform to sequence thousands of individual cells from organoid cultures. With PCA and t-SNE analysis of the high-dimensional data generated from single cell RNA-seq, we characterized two major cell types, i.e. epithelial and mesenchymal cells. Leverage the information from single cell transcriptome profiles, we identified specific niche factors of the Wnt signaling pathways that are activated in different stomach cell lineages. These results suggest that the mesenchymal cell populations provide a potential source of the R-spondin, a Wnt agonist, that sustains the growth of epithelium. Furthermore, we compared cell populations from Cdh1-/-/Trp53-/- and Trp53-/-organoids, and characterized changes on the transcriptome profiles due to the loss of Cdh1, an early oncogenic event in diffuse gastric cancer development. Overall, using organoid model and high-throughput single cell RNA-Seq provides a novel approach to study early tumor transformation and critical cancer-stroma interactions. Citation Format: Jiamin Chen, Noemi Andor, Susan M. Grimes, Billy Lau, Hanlee P. Ji. Single cell RNA sequencing dissects cellular growth factor dependencies and oncogenic driver effects in an organoid model of gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4339. doi:10.1158/1538-7445.AM2017-4339

Abstract 5164: Primary tumor subclones carry somatic mutation signatures of metastasis

We, and others, have recently shown that the vast majority of primary tumors are mosaics of clonal populations of varying sizes, different genetic makeup and distinct phenotypes. If subsets of these clones evolve the ability to migrate from the primary tumor and to survive in blood or lymphatic circulation, these clones can seed distant metastasis. For this study, we have two goals. First, we identify clones with metastatic phenotypes and characterize the somatic mutations that distinguish them from non-metastatic clones. Second, we use these mutation signatures to learn to recognize metastatic clones and to calculate the likelihood that a primary tumor will metastasize. The identification of clones with metastatic phenotypes among heterogeneous tumor populations has so far been limited due to the availability of only single samples from tumors. We overcome this limitation using our previously published algorithm, EXPANDS, to identify clones present at >10% cell frequency within single tumor samples across eight different types of carcinomas. We use TCGA9s exome-sequencing data to characterize the size and genetic content of clones in 453 primary and 23 metastatic tumors. To quantify the metastatic potential of clones, we compare clone size between primary and metastatic tumors. Next, we model the metastatic potential of clones as a function of their specific point mutations and copy number variations and use principal component analysis to select metastasis gene candidates. Finally, we calculate the likelihood that a primary tumor will metastasize, from the number and size of primary tumor clones with high metastatic potential, further referred to as metastatic clones. We validate the prognostic significance of metastatic clone presence in two independent exome-sequencing datasets: a cross-sectional cohort, consisting of 683 primary tumors and a longitudinal cohort, consisting of six matched primary and metastatic tumors from three patients. We identify 102 metastatic driver gene candidates mutated in clonal populations that demonstrate significantly larger expansion in metastatic as compared to primary tumors. These candidates were enriched for genes associated with cell junction, sympathetic nervous system development, cell adhesion and fibronectin function. Metastatic clones were larger and more frequently observed within stage IV primary tumors as compared to stage I-III primary tumors (p-value = 3E-4). Validation in the independent dataset confirmed increased prevalence and penetrance of metastatic clones in the primary tumors of patients who had metastasis compared non-metastatic patients (p-value = 2E-3). In a univariate Cox model, the presence of metastatic clones was a significant risk factor, independent of cancer type (p-value 1E-2). Overall, we identify gene candidates that may be responsible for the metastatic phenotype of certain clones and the potential utility of clonal quantitation as a biomarker of metastatic progression. Citation Format: Noemi Andor, Hanlee P. Ji. Primary tumor subclones carry somatic mutation signatures of metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5164. doi:10.1158/1538-7445.AM2015-5164