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Supplementary Data from Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples Using Pairtree

Cancers are composed of genetically distinct subpopulations of malignant cells. DNA-sequencing data can be used to determine the somatic point mutations specific to each population and build clone trees describing the evolutionary relationships between them. These clone trees can reveal critical points in disease development and inform treatment. Pairtree is a new method that constructs more accurate and detailed clone trees than previously possible using variant allele frequency data from one or more bulk cancer samples. It does so by first building a Pairs Tensor that captures the evolutionary relationships between pairs of subpopulations, and then it uses these relations to constrain clone trees and infer violations of the infinite sites assumption. Pairtree can accurately build clone trees using up to 100 samples per cancer that contain 30 or more subclonal populations. On 14 B-progenitor acute lymphoblastic leukemias, Pairtree replicates or improves upon expert-derived clone tree reconstructions.Significance:Clone trees illustrate the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA-sequencing data from many samples of the same cancer to build more detailed and accurate clone trees than previously possible.See related commentary by Miller, p. 176.This article is highlighted in the In This Issue feature, p. 171.

Metastatic relapse is the major cause of death in pediatric neuroblastoma, where there remains a lack of therapies to target this stage of disease. To understand the molecular mechanisms mediating neuroblastoma metastasis, we developed a mouse model using intracardiac injection and in vivo selection to isolate malignant cell subpopulations with a higher propensity for metastasis to bone and the central nervous system. Gene expression profiling revealed primary and metastatic cells as two distinct cell populations defined by differential expression of 412 genes and of multiple pathways, including CADM1, SPHK1, and YAP/TAZ, whose expression independently predicted survival. In the metastatic subpopulations, a gene signature was defined (MET-75) that predicted survival of neuroblastoma patients with metastatic disease. Mechanistic investigations demonstrated causal roles for CADM1, SPHK1, and YAP/TAZ in mediating metastatic phenotypes in vitro and in vivo. Notably, pharmacologic targeting of SPHK1 or YAP/TAZ was sufficient to inhibit neuroblastoma metastasis in vivo. Overall, we identify gene expression signatures and candidate therapeutics that could improve the treatment of metastatic neuroblastoma. Cancer Res; 77(3); 696–706. ©2017 AACR.

Further details of materials & methods provided in supplementary data.

Legends for Supplementary figures are provided

Supplementary Figure 1 - The metastatic subpopulations display enhanced metastatic burden relative to the SK-N-AS-TR parental cell line in vivo and increased chemoresistance in vitro. Supplementary Figure 2 - GSEA enrichment map show signaling pathways that are differentially regulated between parental and metastatic subtype. Supplementary Figure 3 - Genes identified in enhanced metastatic subpopulations predict survival of human NB patients in multiple datasets. Supplementary Figure 4 - CADM1 over-expression decreases migration of SHEP cell line. Supplementary Figure 5 - Verteporfin (VP), a pharmacological inhibitor of YAP, induces apoptosis and decreases migration of metastatic cells in in vitro and inhibits the metastatic phenotype in vivo. Supplementary Figure 6 - Pharmacologic inhibition of SPHK1 decreases the viability of NB cell lines and STAT3 and NFkB signaling in the enhanced metastatic subpopulations. Supplementary Figure 7 - Metastatic signature (MET-75) predicts poor survival in MYCN non-amplified patients and outperforms randomly generated 75-gene signatures.

Supplementary Table 1 - Summary of metastatic lesions in mice injected with the parental and enhanced metastatic subpopulations of SK-N-AS-TR.

Cancers are composed of genetically distinct subpopulations of malignant cells. DNA-sequencing data can be used to determine the somatic point mutations specific to each population and build clone trees describing the evolutionary relationships between them. These clone trees can reveal critical points in disease development and inform treatment. Pairtree is a new method that constructs more accurate and detailed clone trees than previously possible using variant allele frequency data from one or more bulk cancer samples. It does so by first building a Pairs Tensor that captures the evolutionary relationships between pairs of subpopulations, and then it uses these relations to constrain clone trees and infer violations of the infinite sites assumption. Pairtree can accurately build clone trees using up to 100 samples per cancer that contain 30 or more subclonal populations. On 14 B-progenitor acute lymphoblastic leukemias, Pairtree replicates or improves upon expert-derived clone tree reconstructions. Significance: Clone trees illustrate the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA-sequencing data from many samples of the same cancer to build more detailed and accurate clone trees than previously possible. See related commentary by Miller, p. 176. This article is highlighted in the In This Issue feature, p. 171.

5-10% of patients with the IGHV wild type form of chronic lymphocytic leukemia (CLL) carry a g.3A>C driver mutation in the U1 small nuclear RNA (snRNA). We investigatedthe patterns of mis-splicing and their pathway consequences in three U1-mutant CLL cellline models using long read sequencing in order to better understand the mechanisms ofoncogenicity in tumors carrying this mutation. CLL cell lines (HG3, JVM3 and MEC1) expressing the U1 mutation and their wild-type counterparts were submitted to Oxford Nanopore sequencing to generate full-length RNA transcripts. This transcriptomic datawas evaluated, together with previously obtained short-read data. The g.3A>C mutation occurs at a specific location of the U1 snRNA, a core component of the eukaryotic spliceosome, and acts by altering the 5' splice site (5'SS) recognition sequence to cause consistent patterns of mis-splicing. Long read analysis identified multiple instances of intron retention and suppression of exon skipping, and in silico translation of these mis-splicing events predicted stop-gain and other loss of function mutations in several expressed genes, as well as widespread changes in gene expression levels. We performed a pathway overrepresentation analysis of the altered gene expression patterns in the mutant cells using the Reactome knowledgebase and identified an enrichment in the processes of translation, non-sense mediated decay (NMD) and immune signaling, including interferon signaling. In particular, there was a marked down-regulation of genes related to ribosomal assembly and the translational machinery. A more comprehensiveanalysis of the U1 mutation phenotype in CLL may accelerate the development ofbetter therapeutic and diagnostic approaches in patients. Citation Format: Andrea Senff-Ribeiro, Fatemeh Almodaresi, Quang Trinh, Shimin Shuai, David Spaner, Xose S. Puente, Elias Campo, Lincoln D. Stein. In CLL the U1 snRNA driver mutation alters splicing in multiple genes and pathways. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3814.

The International Cancer Genome Consortium (ICGC) was established to bring together researchers from around the globe to comprehensively analyze the genomic, transcriptomic, and epigenomic changes in 50 different tumor types or subtypes that are of clinical and societal importance across the globe (International network of cancer genome projects. Nature 464, 993-998 (15 April 2010)). As of November 2016, the ICGC has received commitments from researchers and funding organizations in Asia, Australia, Europe, North America and South America for 103 project teams in 17 jurisdictions to study more than 25,000 tumor genomes. Processed data is available via the Data Coordination Centre (http://dcc.icgc.org) based at the Ontario Institute for Cancer Research and is updated semi-annually. The August 2016 release (Version 22) in total comprises data from more than 16,000 cancer donors spanning 70 projects and 21 tumor sites. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the ICGC and The Cancer Genome Atlas (TCGA) is coordinating analysis of more than 2,600 cancer genomes, with the extensive use of cloud computing. Because of the very large size of the pan-cancer dataset, with 5,000 whole genome sequences, PCAWG is using a distributed compute cloud environment (generated by computing centres in the USA, Europe and Asia) that meets the project’s technical requirements and the bioethical framework of ICGC and its member projects. Each genome is being characterized through a suite of standardized algorithms, including alignment to the reference genome, uniform quality assessment, and the calling of multiple classes of somatic mutations. Scientists participating in the research projects of PCAWG are addressing a series of fundamental questions about cancer biology and evolution based on these data. The first phase of ICGC, which is slated for completion in 2018, has focused on developing extensive catalogs of tumor genomic information. The proposed second phase, ICGCmed, will link genomics to clinical information and health, including lifestyle, patient history, response to therapies, and underlying causes of disease, for a broad spectrum of cancers, including preneoplastic lesions, early cancers and metastases. The goal will be to accelerate the movement of genomic information into the clinic to guide prevention, early detection, diagnosis, and prognosis, and provide the information needed to match a patient’s disease to the most effective combinations of therapy. The ICGC develops policies and quality control criteria to help harmonize the work of member projects located in different jurisdictions. Data produced by ICGC projects are made rapidly and freely available to qualified researchers around the world via the data cloud and through the ICGC Data Coordination Center at (http://dcc.icgc.org). More information can be found on www.icgc.org. Citation Format: Jennifer L. Jennings, Lincoln D. Stein, Fabien Calvo. International Cancer Genome Consortium (ICGC) [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 377. doi:10.1158/1538-7445.AM2017-377