The copy-number tree mixture deconvolution problem and applications to multi-sample bulk sequencing tumor data

Cancer is an evolutionary process driven by somatic mutation. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the mutational complexity of cancer and the fact that nearly all cancer sequencing is of bulk tissue, measuring a super-position of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy number data from multiple samples of a tumor. CNTMD generalizes two approaches that have been researched intensively in recent years: deconvolution/factorization algorithms that aim to infer the number and proportions of clones in a mixed tumor sample; and phylogenetic models of copy number evolution that model the dependencies between copy number events that affect the same genomic loci. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that perform either deconvolution or phylogenetic tree construction under the assumption of a single tumor clone per sample. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher-resolution view of copy number evolution of this cancer than published analyses

Bayesian Inference for Genomic Data Analysis

High-throughput genomic data contain gazillion of information that are influenced by the complex biological processes in the cell. As such, appropriate mathematical modeling frameworks are required to understand the data and the data generating processes. This dissertation focuses on the formulation of mathematical models and the description of appropriate computational algorithms to obtain insights from genomic data. Specifically, characterization of intra-tumor heterogeneity is studied. Based on the total number of allele copies at the genomic locations in the tumor subclones, the problem is viewed from two perspectives: the presence or absence of copy-neutrality assumption. With the presence of copy-neutrality, it is assumed that the genome contains mutational variability and the three possible genotypes may be present at each genomic location. As such, the genotypes of all the genomic locations in the tumor subclones are modeled by a ternary matrix. In the second case, in addition to mutational variability, it is assumed that the genomic locations may be affected by structural variabilities such as copy number variation (CNV). Thus, the genotypes are modeled with a pair of (Q + 1)-ary matrices. Using the categorical Indian buffet process (cIBP), state-space modeling framework is employed in describing the two processes and the sequential Monte Carlo (SMC) methods for dynamic models are applied to perform inference on important model parameters. Moreover, the problem of estimating gene regulatory network (GRN) from measurement with missing values is presented. Specifically, gene expression time series data may contain missing values for entire expression values of a single point or some set of consecutive time points. However, complete data is often needed to make inference on the underlying GRN. Using the missing measurement, a dynamic stochastic model is used to describe the evolution of gene expression and point-based Gaussian approximation (PBGA) filters with one-step or two-step missing measurements are applied for the inference. Finally, the problem of deconvolving gene expression data from complex heterogeneous biological samples is examined, where the observed data are a mixture of different cell types. A statistical description of the problem is used and the SMC method for static models is applied to estimate the cell-type specific expressions and the cell type proportions in the heterogeneous samples

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer

TCR repertoire; Breast cancer; Clade mutationsRepertori TCR; Càncer de mama; Mutacions cladeRepertorio TCR; Cáncer de mama; Mutaciones cladoThe detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.CRUK

Computational investigation of cancer genomes

Cancer is a leading cause of death worldwide, and its incidence is increasing due to modern lifestyle that prolonged human life. All cancers originate from a single cell that had acquired genetic aberrations enabling uncontrolled proliferation. Each cancer is unique in its aberrant genetic makeup, which defines, to large extent, its biology, aggressiveness, and vulnerabilities to different treatments. Furthermore, the genetic makeup of each cancer is heterogeneous among its constituent cancer cells, and dynamic with the ability to evolve in order to preserve the survival of cancer cells. Sequencing technologies are currently producing massive amounts of data that, with the help of specialized computational methods, can revolutionize our knowledge on cancer. A key question in cancer research is how to personalize the treatment of cancer patients, so that each cancer is treated according to its molecular characteristics. The first study in this thesis takes a step in that direction through a proposed novel molecular classification system of diffuse large B-cell lymphoma (DLBCL), which is the most common hematological malignancy in adults. The suggested classification, derived from the integrative analysis of gene expression and DNA mutations, stratifies DLBCL into four groups with distinct biology, genetic landscapes, and clinical outcome. These subtypes could help identify patients at high risk who may benefit from an altered treatment plan. Understanding the genomic evolution of cancer that transforms a typically curable primary tumor into an incurable drug-resistant metastasis is another aspect of cancer research under intensive investigation. The second study in this thesis investigates the spreading patterns of metastasis in breast cancer, which is the most common cancer in women. Using phylogenetic analysis of somatic mutations from longitudinal breast cancer samples, the metastasis routes were uncovered. The study revealed that breast cancer spreads either in parallel from primary tumor to multiple distant sites, or linearly from primary tumor to a distant site, and then from that to another. However, in all cases, axillary lymph nodes did not mediate the spreading to distant sites. This provided a genetic-based evidence on the redundancy of lymph node dissection in breast cancer management. Towards a genetic-based diagnostics in cancer, the computational methods used to detect genetic aberrations need to be evaluated for their accuracy. The third study in this thesis performs a comparison of methods for detecting somatic copy number alterations from cancer samples. The study evaluated several commonly used methods for two different sequencing platforms using simulated and real cancer data. The results provided an overview of the weaknesses of the different methods that could be methodologically improved. Altogether, this thesis gives an overview on the field of computational cancer genomics and presents three studies that exemplify the clinical relevance of computational research.Not availabl

Genomic patterns of malignant peripheral nerve sheath tumor (MPNST) evolution correlate with clinical outcome and are detectable in cell-free DNA

Malignant peripheral nerve sheath tumor (MPNST), an aggressive soft-tissue sarcoma, occurs in people with neurofibromatosis type 1 (NF1) and sporadically. Whole-genome and multiregional exome sequencing, transcriptomic, and methylation profiling of 95 tumor samples revealed the order of genomic events in tumor evolution. Following biallelic inactivation of NF1, loss of CDKN2A or TP53 with or without inactivation of polycomb repressive complex 2 (PRC2) leads to extensive somatic copy-number aberrations (SCNA). Distinct pathways of tumor evolution are associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumors with H3K27me3 loss evolve through extensive chromosomal losses followed by whole-genome doubling and chromosome 8 amplification, and show lower levels of immune cell infiltration. Retention of H3K27me3 leads to extensive genomic instability, but an immune cell-rich phenotype. Specific SCNAs detected in both tumor samples and cell-free DNA (cfDNA) act as a surrogate for H3K27me3 loss and immune infiltration, and predict prognosis

Genomic patterns of malignant peripheral nerve sheath tumor (MPNST) evolution correlate with clinical outcome and are detectable in cell-free DNA

UNLABELLED: Malignant peripheral nerve sheath tumor (MPNST), an aggressive soft-tissue sarcoma, occurs in people with neurofibromatosis type 1 (NF1) and sporadically. Whole-genome and multiregional exome sequencing, transcriptomic, and methylation profiling of 95 tumor samples revealed the order of genomic events in tumor evolution. Following biallelic inactivation of NF1, loss of CDKN2A or TP53 with or without inactivation of polycomb repressive complex 2 (PRC2) leads to extensive somatic copy-number aberrations (SCNA). Distinct pathways of tumor evolution are associated with inactivation of PRC2 genes and H3K27 trimethylation (H3K27me3) status. Tumors with H3K27me3 loss evolve through extensive chromosomal losses followed by whole-genome doubling and chromosome 8 amplification, and show lower levels of immune cell infiltration. Retention of H3K27me3 leads to extensive genomic instability, but an immune cell-rich phenotype. Specific SCNAs detected in both tumor samples and cell-free DNA (cfDNA) act as a surrogate for H3K27me3 loss and immune infiltration, and predict prognosis. SIGNIFICANCE: MPNST is the most common cause of death and morbidity for individuals with NF1, a relatively common tumor predisposition syndrome. Our results suggest that somatic copy-number and methylation profiling of tumor or cfDNA could serve as a biomarker for early diagnosis and to stratify patients into prognostic and treatment-related subgroups. This article is highlighted in the In This Issue feature, p. 517

Inference of Tumor Phylogenies from Genomic Assays on Heterogeneous Samples

Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or “progression pathways,” seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling those markers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers